{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T12:00:36Z","timestamp":1780401636877,"version":"3.54.1"},"reference-count":314,"publisher":"Springer Science and Business Media LLC","issue":"8","license":[{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"},{"start":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T00:00:00Z","timestamp":1747094400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["32270690"],"award-info":[{"award-number":["32270690"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100012542","name":"Sichuan Provincial Science and Technology Support Program","doi-asserted-by":"publisher","award":["2024YFHZ0205"],"award-info":[{"award-number":["2024YFHZ0205"]}],"id":[{"id":"10.13039\/100012542","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Artif Intell Rev"],"DOI":"10.1007\/s10462-025-11246-2","type":"journal-article","created":{"date-parts":[[2025,5,13]],"date-time":"2025-05-13T02:05:25Z","timestamp":1747101925000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Face-based machine learning diagnostics: applications, challenges and opportunities"],"prefix":"10.1007","volume":"58","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-1606-0605","authenticated-orcid":false,"given":"Jie","family":"Song","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Mengqiao","family":"He","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Xin","family":"Zheng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Yuxin","family":"Zhang","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Cheng","family":"Bi","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinhua","family":"Feng","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiale","family":"Du","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0009-0003-3651-6076","authenticated-orcid":false,"given":"Hang","family":"Li","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Bairong","family":"Shen","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"297","published-online":{"date-parts":[[2025,5,13]]},"reference":[{"key":"11246_CR1","doi-asserted-by":"crossref","first-page":"17","DOI":"10.1016\/j.yebeh.2018.02.010","volume":"82","author":"D Ahmedt-Aristizabal","year":"2018","unstructured":"Ahmedt-Aristizabal D, Fookes C, Nguyen K, Denman S, Sridharan S, Dionisio S (2018) Deep facial analysis: a new phase i epilepsy evaluation using computer vision. Epilepsy Behav 82:17\u201324","journal-title":"Epilepsy Behav"},{"key":"11246_CR2","doi-asserted-by":"crossref","first-page":"1230","DOI":"10.1007\/s10803-007-0504-z","volume":"38","author":"B Auyeung","year":"2008","unstructured":"Auyeung B, Baron-Cohen S, Wheelwright S, Allison C (2008) The autism spectrum quotient: children\u2019s version (aq-child). J Autism Dev Disord 38:1230\u20131240","journal-title":"J Autism Dev Disord"},{"issue":"4","key":"11246_CR3","doi-asserted-by":"crossref","first-page":"922","DOI":"10.1162\/qss_a_00329","volume":"5","author":"D Abbonato","year":"2024","unstructured":"Abbonato D, Bianchini S, Gargiulo F, Venturini T (2024) Interdisciplinary research in artificial intelligence: lessons from covid-19. Quant Sci Stud 5(4):922\u2013935","journal-title":"Quant Sci Stud"},{"key":"11246_CR4","unstructured":"Alexey D (2020) An image is worth 16x16 words: transformers for image recognition at scale. arXiv preprint arXiv: 2010.11929"},{"issue":"4","key":"11246_CR5","doi-asserted-by":"crossref","first-page":"812","DOI":"10.1177\/17470161241261907","volume":"20","author":"P Andanda","year":"2024","unstructured":"Andanda P, Mlotshwa L (2024) Streamlining the ethical-legal governance of cross-border health data sharing during global health emergencies. Res Ethics 20(4):812\u2013834","journal-title":"Res Ethics"},{"key":"11246_CR6","doi-asserted-by":"crossref","unstructured":"Archila J, Manzanera A, Mart\u00ednez F (2024) A mixed audio-video spd network for online classification of parkinsonian speech patterns. In: Ibero-American conference on artificial intelligence, pp. 110\u2013121. Springer","DOI":"10.1007\/978-3-031-80366-6_10"},{"issue":"S7","key":"11246_CR7","doi-asserted-by":"crossref","first-page":"49","DOI":"10.1192\/S0007125000291496","volume":"155","author":"NC Andreasen","year":"1989","unstructured":"Andreasen NC (1989) The scale for the assessment of negative symptoms (sans): conceptual and theoretical foundations. British J Psychiatry 155(S7):49\u201352","journal-title":"British J Psychiatry"},{"issue":"1","key":"11246_CR8","doi-asserted-by":"crossref","first-page":"37","DOI":"10.1016\/j.jobcr.2018.08.007","volume":"9","author":"N Al-Namnam","year":"2019","unstructured":"Al-Namnam N, Hariri F, Thong M, Rahman Z (2019) Crouzon syndrome: genetic and intervention review. J Oral Biol Craniofacial Res 9(1):37\u201339","journal-title":"J Oral Biol Craniofacial Res"},{"key":"11246_CR9","doi-asserted-by":"crossref","first-page":"1486","DOI":"10.1056\/NEJMlim035027","volume":"348","author":"GJ Annas","year":"2003","unstructured":"Annas GJ (2003) Hipaa regulations: a new era of medical-record privacy? New England J Med 348:1486","journal-title":"New England J Med"},{"key":"11246_CR10","doi-asserted-by":"crossref","unstructured":"American Psychiatric\u00a0Association D, American Psychiatric\u00a0Association D, et al. (2013) Diagnostic and statistical manual of mental disorders: DSM-5 vol. 5. American psychiatric association Washington, DC","DOI":"10.1176\/appi.books.9780890425596"},{"issue":"11","key":"11246_CR11","doi-asserted-by":"crossref","first-page":"710","DOI":"10.3390\/bioengineering9110710","volume":"9","author":"MS Alam","year":"2022","unstructured":"Alam MS, Rashid MM, Roy R, Faizabadi AR, Gupta KD, Ahsan MM (2022) Empirical study of autism spectrum disorder diagnosis using facial images by improved transfer learning approach. Bioengineering 9(11):710","journal-title":"Bioengineering"},{"issue":"18","key":"11246_CR12","doi-asserted-by":"crossref","first-page":"2948","DOI":"10.3390\/diagnostics13182948","volume":"13","author":"B Awaji","year":"2023","unstructured":"Awaji B, Senan EM, Olayah F, Alshari EA, Alsulami M, Abosaq HA, Alqahtani J, Janrao P (2023) Hybrid techniques of facial feature image analysis for early detection of autism spectrum disorder based on combined cnn features. Diagnostics 13(18):2948","journal-title":"Diagnostics"},{"issue":"1","key":"11246_CR13","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1038\/s41572-019-0143-7","volume":"6","author":"SE Antonarakis","year":"2020","unstructured":"Antonarakis SE, Skotko BG, Rafii MS, Strydom A, Pape SE, Bianchi DW, Sherman SL, Reeves RH (2020) Down syndrome. Nat Rev Dis Prim 6(1):9","journal-title":"Nat Rev Dis Prim"},{"key":"11246_CR14","doi-asserted-by":"crossref","unstructured":"Association, A.D.: 2. classification and diagnosis of diabetes: standards of medical care in diabetes\u20132018. Diabetes care 41(Supplement_1), 13\u201327 (2018)","DOI":"10.2337\/dc18-S002"},{"key":"11246_CR15","doi-asserted-by":"crossref","DOI":"10.1016\/j.eclinm.2022.101675","volume":"54","author":"D Arias","year":"2022","unstructured":"Arias D, Saxena S, Verguet S (2022) Quantifying the global burden of mental disorders and their economic value. EClinicalMedicine 54:101675","journal-title":"EClinicalMedicine"},{"key":"11246_CR16","doi-asserted-by":"crossref","first-page":"115","DOI":"10.1016\/j.yebeh.2015.04.015","volume":"47","author":"EE Atao\u011flu","year":"2015","unstructured":"Atao\u011flu EE, Y\u0131ld\u0131r\u0131m \u0130, Bilir E (2015) An evaluation of lateralizing signs in patients with temporal lobe epilepsy. Epilepsy Behav 47:115\u2013119","journal-title":"Epilepsy Behav"},{"issue":"1","key":"11246_CR17","doi-asserted-by":"crossref","first-page":"24699","DOI":"10.2196\/24699","volume":"9","author":"ML Birnbaum","year":"2022","unstructured":"Birnbaum ML, Abrami A, Heisig S, Ali A, Arenare E, Agurto C, Lu N, Kane JM, Cecchi G (2022) Acoustic and facial features from clinical interviews for machine learning-based psychiatric diagnosis: Algorithm development. JMIR Mental Health 9(1):24699","journal-title":"JMIR Mental Health"},{"issue":"4","key":"11246_CR18","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1007\/s41666-021-00101-y","volume":"5","author":"B Banire","year":"2021","unstructured":"Banire B, Al Thani D, Qaraqe M, Mansoor B (2021) Face-based attention recognition model for children with autism spectrum disorder. J Healthcare Inform Res 5(4):420\u2013445","journal-title":"J Healthcare Inform Res"},{"issue":"3","key":"11246_CR19","first-page":"1","volume":"149","author":"RF Baugh","year":"2013","unstructured":"Baugh RF, Basura GJ, Ishii LE, Schwartz SR, Drumheller CM, Burkholder R, Deckard NA, Dawson C, Driscoll C, Gillespie MB et al (2013) Clinical practice guideline: Bell\u2019s palsy. Otolaryngol Head Neck Surg 149(3):1\u201327","journal-title":"Otolaryngol Head Neck Surg"},{"issue":"2","key":"11246_CR20","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1097\/PRS.0000000000001447","volume":"136","author":"CA Banks","year":"2015","unstructured":"Banks CA, Bhama PK, Park J, Hadlock CR, Hadlock TA (2015) Clinician-graded electronic facial paralysis assessment: the eface. Plastic Reconstr Surg 136(2):223\u2013230","journal-title":"Plastic Reconstr Surg"},{"key":"11246_CR21","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1005653411471","volume":"31","author":"S Baron-Cohen","year":"2001","unstructured":"Baron-Cohen S, Wheelwright S, Skinner R, Martin J, Clubley E (2001) The autism-spectrum quotient (aq): evidence from asperger syndrome\/high-functioning autism, malesand females, scientists and mathematicians. J Autism Dev Disord 31:5\u201317","journal-title":"J Autism Dev Disord"},{"issue":"8","key":"11246_CR22","doi-asserted-by":"crossref","first-page":"681","DOI":"10.3109\/00048674.2010.496359","volume":"44","author":"C Bourke","year":"2010","unstructured":"Bourke C, Douglas K, Porter R (2010) Processing of facial emotion expression in major depression: a review. Aust New Zealand J Psychiatry 44(8):681\u2013696","journal-title":"Aust New Zealand J Psychiatry"},{"key":"11246_CR23","doi-asserted-by":"crossref","DOI":"10.1016\/j.knosys.2022.109947","volume":"258","author":"A Bennetot","year":"2022","unstructured":"Bennetot A, Franchi G, Del Ser J, Chatila R, Diaz-Rodriguez N (2022) Greybox Xai: a neural-symbolic learning framework to produce interpretable predictions for image classification. Knowl Based Syst 258:109947","journal-title":"Knowl Based Syst"},{"key":"11246_CR24","unstructured":"Bai S, Kolter JZ, Koltun V (2018) An empirical evaluation of generic convolutional and recurrent networks for sequence modeling. arXiv preprint arXiv:1803.01271"},{"key":"11246_CR25","first-page":"993","volume":"3","author":"DM Blei","year":"2003","unstructured":"Blei DM, Ng AY, Jordan MI (2003) Latent Dirichlet allocation. J Mach Learn Res 3:993\u20131022","journal-title":"J Mach Learn Res"},{"issue":"1","key":"11246_CR26","doi-asserted-by":"crossref","first-page":"23","DOI":"10.1016\/0022-3999(94)90005-1","volume":"38","author":"RM Bagby","year":"1994","unstructured":"Bagby RM, Parker JD, Taylor GJ (1994) The twenty-item Toronto alexithymia scale-i. item selection and cross-validation of the factor structure. J Psychosom Res 38(1):23\u201332","journal-title":"J Psychosom Res"},{"key":"11246_CR27","doi-asserted-by":"crossref","first-page":"1137","DOI":"10.2147\/NDT.S46525","volume":"9","author":"G Bersani","year":"2013","unstructured":"Bersani G, Polli E, Valeriani G, Zullo D, Melcore C, Capra E, Quartini A, Marino P, Minichino A, Bernabei L et al (2013) Facial expression in patients with bipolar disorder and schizophrenia in response to emotional stimuli: a partially shared cognitive and social deficit of the two disorders. Neuropsychiatric Dis Treat 9:1137\u20131144","journal-title":"Neuropsychiatric Dis Treat"},{"key":"11246_CR28","doi-asserted-by":"crossref","first-page":"5","DOI":"10.1023\/A:1010933404324","volume":"45","author":"L Breiman","year":"2001","unstructured":"Breiman L (2001) Random forests. Mach Learn 45:5\u201332","journal-title":"Mach Learn"},{"issue":"4","key":"11246_CR29","doi-asserted-by":"crossref","first-page":"1111","DOI":"10.1109\/JBHI.2020.3019242","volume":"25","author":"A Bandini","year":"2020","unstructured":"Bandini A, Rezaei S, Guar\u00edn DL, Kulkarni M, Lim D, Boulos MI, Zinman L, Yunusova Y, Taati B (2020) A new dataset for facial motion analysis in individuals with neurological disorders. IEEE J Biomed Health Inform 25(4):1111\u20131119","journal-title":"IEEE J Biomed Health Inform"},{"key":"11246_CR30","doi-asserted-by":"crossref","unstructured":"Baltru\u0161aitis T, Robinson P, Morency L-P (2016) Openface: an open source facial behavior analysis toolkit. In: 2016 IEEE Winter conference on applications of computer vision (WACV), pp. 1\u201310. IEEE","DOI":"10.1109\/WACV.2016.7477553"},{"key":"11246_CR31","doi-asserted-by":"crossref","first-page":"96","DOI":"10.1016\/0022-510X(94)90191-0","volume":"124","author":"BR Brooks","year":"1994","unstructured":"Brooks BR (1994) El escorial world federation of neurology criteria for the diagnosis of amyotrophic lateral sclerosis. Subcommittee on motor neuron diseases\/amyotrophic lateral sclerosis of the world federation of neurology research group on neuromuscular diseases and the el escorial\" clinical limits of amyotrophic lateral sclerosis\" workshop contributors. J Neurol Sci 124:96\u2013107","journal-title":"J Neurol Sci"},{"issue":"S11","key":"11246_CR32","first-page":"10","volume":"44","author":"DJ Brooks","year":"1998","unstructured":"Brooks DJ (1998) The early diagnosis of Parkinson\u2019s disease. Ann. Neurol. 44(S11):10\u201318","journal-title":"Ann. Neurol."},{"key":"11246_CR33","doi-asserted-by":"crossref","unstructured":"Beck AT, Steer RA, Brown G (1996) Beck depression inventory\u2013ii. Psychological assessment (1996)","DOI":"10.1037\/t00742-000"},{"key":"11246_CR34","doi-asserted-by":"crossref","DOI":"10.1016\/j.imavis.2023.104688","volume":"135","author":"F Boutros","year":"2023","unstructured":"Boutros F, Struc V, Fierrez J, Damer N (2023) Synthetic data for face recognition: current state and future prospects. Image Vis Comput 135:104688","journal-title":"Image Vis Comput"},{"issue":"1","key":"11246_CR35","first-page":"4583895","volume":"2019","author":"V Blanes-Vidal","year":"2019","unstructured":"Blanes-Vidal V, Majtner T, Avenda\u00f1o-Valencia LD, Yderstraede KB, Nadimi ES (2019) Invisible color variations of facial erythema: a novel early marker for diabetic complications? J Diabetes Res 2019(1):4583895","journal-title":"J Diabetes Res"},{"issue":"7","key":"11246_CR36","doi-asserted-by":"crossref","first-page":"3229","DOI":"10.1109\/JBHI.2022.3164848","volume":"26","author":"JJ Bannister","year":"2022","unstructured":"Bannister JJ, Wilms M, Aponte JD, Katz DC, Klein OD, Bernier FP, Spritz RA, Hallgr\u00edmsson B, Forkert ND (2022) A deep invertible 3-d facial shape model for interpretable genetic syndrome diagnosis. IEEE J Biomed Health Inform 26(7):3229\u20133239","journal-title":"IEEE J Biomed Health Inform"},{"key":"11246_CR37","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1016\/j.jpeds.2017.01.021","volume":"183","author":"K Campbell","year":"2017","unstructured":"Campbell K, Carpenter KL, Espinosa S, Hashemi J, Qiu Q, Tepper M, Calderbank R, Sapiro G, Egger HL, Baker JP et al (2017) Use of a digital modified checklist for autism in toddlers-revised with follow-up to improve quality of screening for autism. J Pediatr 183:133\u2013139","journal-title":"J Pediatr"},{"key":"11246_CR38","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2021.106288","volume":"208","author":"F Cabitza","year":"2021","unstructured":"Cabitza F, Campagner A, Soares F, Guadiana-Romualdo LG, Challa F, Sulejmani A, Seghezzi M, Carobene A (2021) The importance of being external methodological insights for the external validation of machine learning models in medicine. Comput Methods Prog Biomed 208:106288","journal-title":"Comput Methods Prog Biomed"},{"issue":"3","key":"11246_CR39","doi-asserted-by":"crossref","first-page":"2466","DOI":"10.21037\/qims-22-1108","volume":"14","author":"Y Chong","year":"2024","unstructured":"Chong Y, Du F, Ma X, An Y, Huang Q, Long X, Huang J, Li Z, Yu N, Wang X (2024) Automated anatomical landmark detection on 3d facial images using u-net-based deep learning algorithm. Quant Imaging Med Surg 14(3):2466","journal-title":"Quant Imaging Med Surg"},{"key":"11246_CR40","doi-asserted-by":"crossref","unstructured":"Campbell J, Dawson M, Zisserman A, Xie W, Nell\u00e5ker C (2023) Deep facial phenotyping with mixup augmentation. In: Annual conference on medical image understanding and analysis, pp. 133\u2013144. Springer","DOI":"10.1007\/978-3-031-48593-0_10"},{"key":"11246_CR41","doi-asserted-by":"crossref","unstructured":"Chen T, Guestrin C (2016) Xgboost: A scalable tree boosting system. In: Proceedings of the 22nd Acm Sigkdd international conference on knowledge discovery and data mining, pp. 785\u2013794","DOI":"10.1145\/2939672.2939785"},{"issue":"3","key":"11246_CR42","doi-asserted-by":"crossref","first-page":"488","DOI":"10.1002\/aur.2391","volume":"14","author":"KL Carpenter","year":"2021","unstructured":"Carpenter KL, Hahemi J, Campbell K, Lippmann SJ, Baker JP, Egger HL, Espinosa S, Vermeer S, Sapiro G, Dawson G (2021) Digital behavioral phenotyping detects atypical pattern of facial expression in toddlers with autism. Autism Res 14(3):488\u2013499","journal-title":"Autism Res"},{"issue":"1","key":"11246_CR43","doi-asserted-by":"crossref","first-page":"162","DOI":"10.1038\/s41746-022-00700-y","volume":"5","author":"T Ciecierski-Holmes","year":"2022","unstructured":"Ciecierski-Holmes T, Singh R, Axt M, Brenner S, Barteit S (2022) Artificial intelligence for strengthening healthcare systems in low-and middle-income countries: a systematic scoping review. NPJ Digital Med 5(1):162","journal-title":"NPJ Digital Med"},{"issue":"4","key":"11246_CR44","doi-asserted-by":"crossref","first-page":"781","DOI":"10.1007\/s42761-023-00191-4","volume":"4","author":"JH Cheong","year":"2023","unstructured":"Cheong JH, Jolly E, Xie T, Byrne S, Kenney M, Chang LJ (2023) Py-feat: python facial expression analysis toolbox. Affect Sci 4(4):781\u2013796","journal-title":"Affect Sci"},{"issue":"6","key":"11246_CR45","doi-asserted-by":"crossref","first-page":"711","DOI":"10.1038\/s41551-022-00988-x","volume":"7","author":"M Chua","year":"2023","unstructured":"Chua M, Kim D, Choi J, Lee NG, Deshpande V, Schwab J, Lev MH, Gonzalez RG, Gee MS, Do S (2023) Tackling prediction uncertainty in machine learning for healthcare. Nat Biomed Eng 7(6):711\u2013718","journal-title":"Nat Biomed Eng"},{"issue":"4","key":"11246_CR46","doi-asserted-by":"crossref","first-page":"3191","DOI":"10.1109\/TAFFC.2022.3181033","volume":"14","author":"N Churamani","year":"2022","unstructured":"Churamani N, Kara O, Gunes H (2022) Domain-incremental continual learning for mitigating bias in facial expression and action unit recognition. IEEE Trans Affect Comput 14(4):3191\u20133206","journal-title":"IEEE Trans Affect Comput"},{"key":"11246_CR47","doi-asserted-by":"crossref","unstructured":"Chu L, Liu Y, Wu Z, Tang S, Chen G, Hao Y, Peng J, Yu Z, Chen Z, Lai B, et al (2022) Pp-humanseg: Connectivity-aware portrait segmentation with a large-scale teleconferencing video dataset. In: Proceedings of the IEEE\/CVF winter conference on applications of computer vision, pp. 202\u2013209","DOI":"10.1109\/WACVW54805.2022.00026"},{"issue":"18","key":"11246_CR48","doi-asserted-by":"crossref","first-page":"960","DOI":"10.21037\/atm-22-3580","volume":"10","author":"B Cheng","year":"2022","unstructured":"Cheng B, Ma J, Chen X, Yuan L (2022) Objective study of the facial parameters of observations in patients with type 2 diabetes mellitus by machine learning. Ann Trans Med 10(18):960","journal-title":"Ann Trans Med"},{"key":"11246_CR49","volume":"80","author":"T Cai","year":"2022","unstructured":"Cai T, Ni H, Yu M, Huang X, Wong K, Volpi J, Wang JZ, Wong ST (2022) Deepstroke: an efficient stroke screening framework for emergency rooms with multimodal adversarial deep learning. Med Image Anal 80:102522","journal-title":"Med Image Anal"},{"key":"11246_CR50","doi-asserted-by":"crossref","unstructured":"Cortes C (1995) Support-vector networks. Machine Learning","DOI":"10.1007\/BF00994018"},{"issue":"1","key":"11246_CR51","doi-asserted-by":"crossref","first-page":"8114049","DOI":"10.1155\/2022\/8114049","volume":"2022","author":"G Cardozo","year":"2022","unstructured":"Cardozo G, Pintarelli GB, Andreis GR, Lopes ACW, Marques JLB (2022) Use of machine learning and routine laboratory tests for diabetes mellitus screening. BioMed Res Int 2022(1):8114049","journal-title":"BioMed Res Int"},{"key":"11246_CR52","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1750-1172-3-17","volume":"3","author":"P Chanson","year":"2008","unstructured":"Chanson P, Salenave S (2008) Acromegaly. Orphanet J Rare Dis 3:1\u201317","journal-title":"Orphanet J Rare Dis"},{"issue":"1\u20132","key":"11246_CR53","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1016\/S0022-510X(99)00210-5","volume":"169","author":"JM Cedarbaum","year":"1999","unstructured":"Cedarbaum JM, Stambler N, Malta E, Fuller C, Hilt D, Thurmond B, Nakanishi A, Group BAS et al (1999) The alsfrs-r: a revised als functional rating scale that incorporates assessments of respiratory function. J Neurol Sci 169(1\u20132):13\u201321","journal-title":"J Neurol Sci"},{"issue":"3","key":"11246_CR54","first-page":"288","volume":"11","author":"P Chagas","year":"2023","unstructured":"Chagas P, Souza L, Pontes I, Calumby R, Angelo M, Duarte A, Lc-Dos Santos W, Oliveira L (2023) Uncertainty-aware membranous nephropathy classification: a monte-carlo dropout approach to detect how certain is the model. Comput Methods Biomech Biomed Eng 11(3):288\u2013298","journal-title":"Comput Methods Biomech Biomed Eng"},{"key":"11246_CR55","doi-asserted-by":"crossref","unstructured":"Cao Q, Shen L, Xie W, Parkhi OM, Zisserman A (2018) Vggface2: a dataset for recognising faces across pose and age. In: 2018 13th IEEE international conference on automatic face & gesture recognition (FG 2018), pp. 67\u201374. IEEE","DOI":"10.1109\/FG.2018.00020"},{"key":"11246_CR56","doi-asserted-by":"crossref","first-page":"205520762412339","DOI":"10.1177\/20552076241233998","volume":"10","author":"K Clo\u00df","year":"2024","unstructured":"Clo\u00df K, Verket M, M\u00fcller-Wieland D, Marx N, Schuett K, Jost E, Crysandt M, Beier F, Br\u00fcmmendorf TH, Kobbe G et al (2024) Application of wearables for remote monitoring of oncology patients: a scoping review. Digital Health 10:20552076241234000","journal-title":"Digital Health"},{"issue":"6","key":"11246_CR57","doi-asserted-by":"crossref","first-page":"719","DOI":"10.1038\/s41551-023-01056-8","volume":"7","author":"RJ Chen","year":"2023","unstructured":"Chen RJ, Wang JJ, Williamson DF, Chen TY, Lipkova J, Lu MY, Sahai S, Mahmood F (2023) Algorithmic fairness in artificial intelligence for medicine and healthcare. Nat Biomed Eng 7(6):719\u2013742","journal-title":"Nat Biomed Eng"},{"key":"11246_CR58","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Mane D, Vasudevan V, Le QV (2019) Autoaugment: learning augmentation strategies from data. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition, pp. 113\u2013123","DOI":"10.1109\/CVPR.2019.00020"},{"key":"11246_CR59","doi-asserted-by":"crossref","unstructured":"Cubuk ED, Zoph B, Shlens J, Le QV (2020) Randaugment: practical automated data augmentation with a reduced search space. In: Proceedings of the IEEE\/CVF conference on computer vision and pattern recognition workshops, pp. 702\u2013703 (2020)","DOI":"10.1109\/CVPRW50498.2020.00359"},{"key":"11246_CR60","unstructured":"DeVault D, Artstein R, Benn G, Dey T, Fast E, Gainer A, Georgila K, Gratch J, Hartholt A, Lhommet M, et al (2014) Simsensei kiosk: A virtual human interviewer for healthcare decision support. In: Proceedings of the 2014 international conference on autonomous agents and multi-agent systems, pp. 1061\u20131068"},{"key":"11246_CR61","doi-asserted-by":"crossref","unstructured":"Dip SA, Arif KHI, Shuvo UA, Khan IA, Meng N (2024) Equitable skin disease prediction using transfer learning and domain adaptation. In: Proceedings of the AAAI Symposium Series, vol. 4, pp. 259\u2013266","DOI":"10.1609\/aaaiss.v4i1.31800"},{"key":"11246_CR62","unstructured":"Davis M (1980) A multidimensional approach to individual differences in empathy. JSAS Catalog of Selected Documents in Psychology"},{"key":"11246_CR63","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13229-021-00430-0","volume":"12","author":"H Drimalla","year":"2021","unstructured":"Drimalla H, Baskow I, Behnia B, Roepke S, Dziobek I (2021) Imitation and recognition of facial emotions in autism: a computer vision approach. Mol Autism 12:1\u201315","journal-title":"Mol Autism"},{"key":"11246_CR64","doi-asserted-by":"crossref","unstructured":"Deng J, Guo J, Xue N, Zafeiriou S (2019) Arcface: Additive angular margin loss for deep face recognition. In: Proceedings of the IEEE\/CVF Conference on computer vision and pattern recognition, pp. 4690\u20134699 (2019)","DOI":"10.1109\/CVPR.2019.00482"},{"issue":"6","key":"11246_CR65","doi-asserted-by":"crossref","first-page":"386","DOI":"10.1016\/S2589-7500(24)00050-5","volume":"6","author":"R Daniel","year":"2024","unstructured":"Daniel R, Jones H, Gregory JW, Shetty A, Francis N, Paranjothy S, Townson J (2024) Predicting type 1 diabetes in children using electronic health records in primary care in the UK: development and validation of a machine-learning algorithm. Lancet Digital Health 6(6):386\u2013395","journal-title":"Lancet Digital Health"},{"issue":"1","key":"11246_CR66","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1111\/j.2517-6161.1977.tb01600.x","volume":"39","author":"AP Dempster","year":"1977","unstructured":"Dempster AP, Laird NM, Rubin DB (1977) Maximum likelihood from incomplete data via the em algorithm. J Royal Stat Soc 39(1):1\u201322","journal-title":"J Royal Stat Soc"},{"key":"11246_CR67","first-page":"8780","volume":"34","author":"P Dhariwal","year":"2021","unstructured":"Dhariwal P, Nichol A (2021) Diffusion models beat GANS on image synthesis. Adv Neural Inform Proc Syst 34:8780\u20138794","journal-title":"Adv Neural Inform Proc Syst"},{"key":"11246_CR68","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1750-1172-8-1","volume":"8","author":"Natural history of sanfilippo syndrome in spain","year":"2013","unstructured":"Natural history of sanfilippo syndrome in spain (2013) Delgadillo, V., O\u2019Callaghan, M.d.M., Gort, L., Coll, M.J., Pineda. M. Orphanet J Rare Dis 8:1\u201311","journal-title":"M. Orphanet J Rare Dis"},{"key":"11246_CR69","doi-asserted-by":"crossref","first-page":"58","DOI":"10.1016\/j.inffus.2021.09.022","volume":"79","author":"N D\u00edaz-Rodr\u00edguez","year":"2022","unstructured":"D\u00edaz-Rodr\u00edguez N, Lamas A, Sanchez J, Franchi G, Donadello I, Tabik S, Filliat D, Cruz P, Montes R, Herrera F (2022) Explainable neural-symbolic learning (x-nesyl) methodology to fuse deep learning representations with expert knowledge graphs: the monumai cultural heritage use case. Inform Fusion 79:58\u201383","journal-title":"Inform Fusion"},{"issue":"10132","key":"11246_CR70","doi-asserted-by":"crossref","first-page":"1783","DOI":"10.1016\/S0140-6736(18)30697-4","volume":"391","author":"JL Dieleman","year":"2018","unstructured":"Dieleman JL, Sadat N, Chang AY, Fullman N, Abbafati C, Acharya P, Adou AK, Kiadaliri AA, Alam K, Alizadeh-Navaei R et al (2018) Trends in future health financing and coverage: future health spending and universal health coverage in 188 countries, 2016\u201340. The Lancet 391(10132):1783\u20131798","journal-title":"The Lancet"},{"issue":"1","key":"11246_CR71","doi-asserted-by":"crossref","first-page":"351","DOI":"10.1002\/ima.22461","volume":"31","author":"S Dash","year":"2021","unstructured":"Dash S, Senapati MR, Sahu PK, Chowdary P (2021) Illumination normalized based technique for retinal blood vessel segmentation. Int J Imaging Syst Technol 31(1):351\u2013363","journal-title":"Int J Imaging Syst Technol"},{"key":"11246_CR72","doi-asserted-by":"crossref","unstructured":"Dalal N, Triggs B (2005) Histograms of oriented gradients for human detection. In: 2005 IEEE computer society conference on computer vision and pattern recognition (CVPR\u201905), vol. 1, pp. 886\u2013893. Ieee","DOI":"10.1109\/CVPR.2005.177"},{"key":"11246_CR73","unstructured":"EURORDIS AK, Faurisson F (2009) The Voice of 12,000 Patients. Experiences and Expectations of Rare Disease Patients on Diagnosis and Care in Europe. EURORDIS-Rare Diseases Eu"},{"key":"11246_CR74","volume-title":"Non-verbal communication in depression","author":"H Ellgring","year":"2007","unstructured":"Ellgring H (2007) Non-verbal communication in depression. Cambridge University Press, Cambridge"},{"issue":"2","key":"11246_CR75","doi-asserted-by":"crossref","first-page":"179","DOI":"10.1207\/s15516709cog1402_1","volume":"14","author":"JL Elman","year":"1990","unstructured":"Elman JL (1990) Finding structure in time. Cogn Sci 14(2):179\u2013211","journal-title":"Cogn Sci"},{"key":"11246_CR76","first-page":"333","volume-title":"Mayo clinic proceedings","author":"N Ershadinia","year":"2022","unstructured":"Ershadinia N, Tritos NA (2022) Diagnosis and treatment of acromegaly: an update. Mayo clinic proceedings, vol 97. Elsevier, Amsterdam, pp 333\u2013346"},{"key":"11246_CR77","doi-asserted-by":"crossref","unstructured":"Eyben F, W\u00f6llmer M, Schuller B (2010) Opensmile: the munich versatile and fast open-source audio feature extractor. In: Proceedings of the 18th ACM International Conference on Multimedia, pp. 1459\u20131462 (2010)","DOI":"10.1145\/1873951.1874246"},{"issue":"3","key":"11246_CR78","first-page":"20240025","volume":"3","author":"M Fahaad Almufareh","year":"2024","unstructured":"Fahaad Almufareh M, Tehsin S, Humayun M, Kausar S (2024) Facial classification for autism spectrum disorder. J Disabil Res 3(3):20240025","journal-title":"J Disabil Res"},{"issue":"11","key":"11246_CR79","doi-asserted-by":"crossref","first-page":"804","DOI":"10.1016\/S2213-8587(22)00244-3","volume":"10","author":"M Fleseriu","year":"2022","unstructured":"Fleseriu M, Langlois F, Lim DST, Varlamov EV, Melmed S (2022) Acromegaly: pathogenesis, diagnosis, and management. Lancet Diabetes Endocrinol 10(11):804\u2013826","journal-title":"Lancet Diabetes Endocrinol"},{"key":"11246_CR80","first-page":"1189","volume":"25","author":"JH Friedman","year":"2001","unstructured":"Friedman JH (2001) Greedy function approximation: a gradient boosting machine. Ann Stat 25:1189\u20131232","journal-title":"Ann Stat"},{"issue":"1","key":"11246_CR81","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1006\/jcss.1997.1504","volume":"55","author":"Y Freund","year":"1997","unstructured":"Freund Y, Schapire RE (1997) A decision-theoretic generalization of on-line learning and an application to boosting. J Comput Syst Sci 55(1):119\u2013139","journal-title":"J Comput Syst Sci"},{"key":"11246_CR82","doi-asserted-by":"crossref","first-page":"02020","DOI":"10.7554\/eLife.02020","volume":"3","author":"Q Ferry","year":"2014","unstructured":"Ferry Q, Steinberg J, Webber C, FitzPatrick DR, Ponting CP, Zisserman A, Nell\u00e5ker C (2014) Diagnostically relevant facial gestalt information from ordinary photos. Elife 3:02020","journal-title":"Elife"},{"issue":"9","key":"11246_CR83","doi-asserted-by":"crossref","first-page":"1697","DOI":"10.1111\/j.1528-1167.2007.01129.x","volume":"48","author":"A Fogarasi","year":"2007","unstructured":"Fogarasi A, Tuxhorn I, Janszky J, Janszky I, R\u00e1sonyi G, Kelemen A, Hal\u00e1sz P (2007) Age-dependent seizure semiology in temporal lobe epilepsy. Epilepsia 48(9):1697\u20131702","journal-title":"Epilepsia"},{"key":"11246_CR84","doi-asserted-by":"crossref","unstructured":"Flores R, Tlachac M, Shrestha A, Rundensteiner E (2022) Temporal facial features for depression screening. In: Adjunct Proceedings of the 2022 ACM International Joint Conference on Pervasive and Ubiquitous Computing and the 2022 ACM International Symposium on Wearable Computers, pp. 488\u2013493","DOI":"10.1145\/3544793.3563424"},{"issue":"2","key":"11246_CR85","doi-asserted-by":"crossref","first-page":"165","DOI":"10.1001\/jamaneurol.2020.4152","volume":"78","author":"VL Feigin","year":"2021","unstructured":"Feigin VL, Vos T, Alahdab F, Amit AML, B\u00e4rnighausen TW, Beghi E, Beheshti M, Chavan PP, Criqui MH, Desai R et al (2021) Burden of neurological disorders across the us from 1990\u20132017: a global burden of disease study. JAMA Neurol 78(2):165\u2013176","journal-title":"JAMA Neurol"},{"key":"11246_CR86","doi-asserted-by":"crossref","unstructured":"Goldberg CB, Adams L, Blumenthal D, Brennan PF, Brown N, Butte AJ, Cheatham M, DeBronkart D, Dixon J, Drazen J, et al (2024) To do no harm\u00e2\u20ac\u201dand the most good\u2013with AI in health care. Massachusetts Medical Society","DOI":"10.1056\/AIp2400036"},{"key":"11246_CR87","unstructured":"Gratch J, Artstein R, Lucas GM, Stratou G, Scherer S, Nazarian A, Wood R, Boberg J, DeVault D, Marsella S, et al (2014) The distress analysis interview corpus of human and computer interviews. In: LREC, pp. 3123\u20133128. Reykjavik"},{"issue":"10","key":"11246_CR88","doi-asserted-by":"crossref","first-page":"641","DOI":"10.1016\/j.imavis.2013.12.007","volume":"32","author":"JM Girard","year":"2014","unstructured":"Girard JM, Cohn JF, Mahoor MH, Mavadati SM, Hammal Z, Rosenwald DP (2014) Nonverbal social withdrawal in depression: evidence from manual and automatic analyses. Image Vis Comput 32(10):641\u2013647","journal-title":"Image Vis Comput"},{"key":"11246_CR89","unstructured":"Gal Y, Ghahramani Z (2016) Dropout as a bayesian approximation: Representing model uncertainty in deep learning. In: International Conference on Machine Learning, pp. 1050\u20131059. PMLR"},{"issue":"1","key":"11246_CR90","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1002\/lary.27986","volume":"130","author":"JJ Greene","year":"2020","unstructured":"Greene JJ, Guarin DL, Tavares J, Fortier E, Robinson M, Dusseldorp J, Quatela O, Jowett N, Hadlock T (2020) The spectrum of facial palsy: The Meei facial palsy photo and video standard set. The Laryngoscope 130(1):32\u201337","journal-title":"The Laryngoscope"},{"issue":"5","key":"11246_CR91","doi-asserted-by":"crossref","first-page":"480","DOI":"10.1016\/S1474-4422(21)00465-8","volume":"21","author":"SA Goutman","year":"2022","unstructured":"Goutman SA, Hardiman O, Al-Chalabi A, Chi\u00f3 A, Savelieff MG, Kiernan MC, Feldman EL (2022) Recent advances in the diagnosis and prognosis of amyotrophic lateral sclerosis. Lancet Neurol 21(5):480\u2013493","journal-title":"Lancet Neurol"},{"issue":"1","key":"11246_CR92","doi-asserted-by":"crossref","first-page":"60","DOI":"10.1038\/s41591-018-0279-0","volume":"25","author":"Y Gurovich","year":"2019","unstructured":"Gurovich Y, Hanani Y, Bar O, Nadav G, Fleischer N, Gelbman D, Basel-Salmon L, Krawitz PM, Kamphausen SB, Zenker M et al (2019) Identifying facial phenotypes of genetic disorders using deep learning. Nat Med 25(1):60\u201364","journal-title":"Nat Med"},{"issue":"2","key":"11246_CR93","doi-asserted-by":"crossref","first-page":"0281248","DOI":"10.1371\/journal.pone.0281248","volume":"18","author":"LF Gomez","year":"2023","unstructured":"Gomez LF, Morales A, Fierrez J, Orozco-Arroyave JR (2023) Exploring facial expressions and action unit domains for Parkinson detection. Plos one 18(2):0281248","journal-title":"Plos one"},{"key":"11246_CR94","doi-asserted-by":"crossref","unstructured":"Gupta R, Malandrakis N, Xiao B, Guha T, Van\u00a0Segbroeck M, Black M, Potamianos A, Narayanan S (2014) Multimodal prediction of affective dimensions and depression in human-computer interactions. In: Proceedings of the 4th International Workshop on Audio\/visual Emotion Challenge, pp. 33\u201340","DOI":"10.1145\/2661806.2661810"},{"key":"11246_CR95","volume":"81","author":"F Guo","year":"2022","unstructured":"Guo F, Ng M, Kuling G, Wright G (2022) Cardiac MRI segmentation with sparse annotations: Ensembling deep learning uncertainty and shape priors. Med Image Anal 81:102532","journal-title":"Med Image Anal"},{"issue":"11","key":"11246_CR96","doi-asserted-by":"crossref","first-page":"139","DOI":"10.1145\/3422622","volume":"63","author":"I Goodfellow","year":"2020","unstructured":"Goodfellow I, Pouget-Abadie J, Mirza M, Xu B, Warde-Farley D, Ozair S, Courville A, Bengio Y (2020) Generative adversarial networks. Commun ACM 63(11):139\u2013144","journal-title":"Commun ACM"},{"issue":"10","key":"11246_CR97","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1109\/TMI.2012.2206398","volume":"31","author":"W G\u00f3mez","year":"2012","unstructured":"G\u00f3mez W, Pereira WCA, Infantosi AFC (2012) Analysis of co-occurrence texture statistics as a function of gray-level quantization for classifying breast ultrasound. IEEE Trans Med Imaging 31(10):1889\u20131899","journal-title":"IEEE Trans Med Imaging"},{"issue":"3","key":"11246_CR98","doi-asserted-by":"crossref","first-page":"109","DOI":"10.1026\/0932-4089.48.3.109","volume":"48","author":"A Graf","year":"2004","unstructured":"Graf A (2004) Eine deutschsprachige version der self-monitoring-skala. Zeitschrift F\u00fcr Arbeits-und Organisationspsychologie A &O 48(3):109\u2013121","journal-title":"Zeitschrift F\u00fcr Arbeits-und Organisationspsychologie A &O"},{"key":"11246_CR99","doi-asserted-by":"crossref","first-page":"469","DOI":"10.1023\/B:JACP.0000037777.17698.01","volume":"32","author":"TF Gross","year":"2004","unstructured":"Gross TF (2004) The perception of four basic emotions in human and nonhuman faces by children with autism and other developmental disabilities. J Abnormal Child Psychol 32:469\u2013480","journal-title":"J Abnormal Child Psychol"},{"issue":"19","key":"11246_CR100","doi-asserted-by":"crossref","first-page":"6595","DOI":"10.3390\/s21196595","volume":"21","author":"M Geremek","year":"2021","unstructured":"Geremek M, Szklanny K (2021) Deep learning-based analysis of face images as a screening tool for genetic syndromes. Sensors 21(19):6595","journal-title":"Sensors"},{"key":"11246_CR101","unstructured":"Goodfellow IJ, Shlens J, Szegedy C (2014) Explaining and harnessing adversarial examples. arXiv preprint arXiv:1412.6572"},{"issue":"12","key":"11246_CR102","doi-asserted-by":"crossref","first-page":"4667","DOI":"10.1044\/2022_JSLHR-22-00072","volume":"65","author":"DL Guarin","year":"2022","unstructured":"Guarin DL, Taati B, Abrahao A, Zinman L, Yunusova Y (2022) Video-based facial movement analysis in the assessment of bulbar amyotrophic lateral sclerosis: clinical validation. J Speech Lang Hearing Res 65(12):4667\u20134678","journal-title":"J Speech Lang Hearing Res"},{"issue":"15","key":"11246_CR103","doi-asserted-by":"crossref","first-page":"2129","DOI":"10.1002\/mds.22340","volume":"23","author":"CG Goetz","year":"2008","unstructured":"Goetz CG, Tilley BC, Shaftman SR, Stebbins GT, Fahn S, Martinez-Martin P, Poewe W, Sampaio C, Stern MB, Dodel R et al (2008) Movement disorder society-sponsored revision of the unified Parkinson\u2019s disease rating scale (mds-updrs): scale presentation and clinimetric testing results. Mov Disord 23(15):2129\u20132170","journal-title":"Mov Disord"},{"issue":"1","key":"11246_CR104","doi-asserted-by":"crossref","first-page":"65","DOI":"10.1186\/s12938-022-01036-0","volume":"21","author":"A Gaber","year":"2022","unstructured":"Gaber A, Taher MF, Wahed MA, Shalaby NM, Gaber S (2022) Classification of facial paralysis based on machine learning techniques. BioMed Eng OnLine 21(1):65","journal-title":"BioMed Eng OnLine"},{"issue":"8","key":"11246_CR105","doi-asserted-by":"crossref","first-page":"1228","DOI":"10.1176\/ajp.152.8.1228","volume":"152","author":"SB Guze","year":"1995","unstructured":"Guze SB (1995) Diagnostic and statistical manual of mental disorders, (dsm-iv). Am J Psychiatry 152(8):1228\u20131228","journal-title":"Am J Psychiatry"},{"issue":"1","key":"11246_CR106","doi-asserted-by":"crossref","first-page":"14","DOI":"10.1109\/TAFFC.2016.2578316","volume":"9","author":"T Guha","year":"2016","unstructured":"Guha T, Yang Z, Grossman RB, Narayanan SS (2016) A computational study of expressive facial dynamics in children with autism. IEEE Trans Affect Comput 9(1):14\u201320","journal-title":"IEEE Trans Affect Comput"},{"issue":"1","key":"11246_CR107","doi-asserted-by":"crossref","first-page":"42","DOI":"10.1089\/fpsam.2019.29000.gua","volume":"22","author":"DL Guarin","year":"2020","unstructured":"Guarin DL, Yunusova Y, Taati B, Dusseldorp JR, Mohan S, Tavares J, Veen MM, Fortier E, Hadlock TA, Jowett N (2020) Toward an automatic system for computer-aided assessment in facial palsy. Facial Plastic Surg Aesthetic Med 22(1):42\u201349","journal-title":"Facial Plastic Surg Aesthetic Med"},{"key":"11246_CR108","doi-asserted-by":"crossref","unstructured":"Guo Y, Zhang L, Hu Y, He X, Gao J (2016) Ms-celeb-1m: A dataset and benchmark for large-scale face recognition. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11-14, 2016, Proceedings, Part III 14, pp. 87\u2013102. Springer","DOI":"10.1007\/978-3-319-46487-9_6"},{"issue":"10","key":"11246_CR109","doi-asserted-by":"crossref","first-page":"1682","DOI":"10.1038\/s41436-020-0845-y","volume":"22","author":"B Hallgr\u00edmsson","year":"2020","unstructured":"Hallgr\u00edmsson B, Aponte JD, Katz DC, Bannister JJ, Riccardi SL, Mahasuwan N, McInnes BL, Ferrara TM, Lipman DM, Neves AB et al (2020) Automated syndrome diagnosis by three-dimensional facial imaging. Genet Med 22(10):1682\u20131693","journal-title":"Genet Med"},{"issue":"1","key":"11246_CR110","doi-asserted-by":"crossref","first-page":"56","DOI":"10.1136\/jnnp.23.1.56","volume":"23","author":"M Hamilton","year":"1960","unstructured":"Hamilton M (1960) A rating scale for depression. J Neurol Neurosurg Psychiatry 23(1):56","journal-title":"J Neurol Neurosurg Psychiatry"},{"issue":"3","key":"11246_CR111","doi-asserted-by":"crossref","first-page":"349","DOI":"10.1038\/s41588-021-01010-x","volume":"54","author":"T-C Hsieh","year":"2022","unstructured":"Hsieh T-C, Bar-Haim A, Moosa S, Ehmke N, Gripp KW, Pantel JT, Danyel M, Mensah MA, Horn D, Rosnev S et al (2022) Gestaltmatcher facilitates rare disease matching using facial phenotype descriptors. Nat Gen 54(3):349\u2013357","journal-title":"Nat Gen"},{"key":"11246_CR112","doi-asserted-by":"crossref","DOI":"10.1016\/j.jbi.2024.104622","volume":"151","author":"Y Huang","year":"2024","unstructured":"Huang Y, Guo J, Chen W-H, Lin H-Y, Tang H, Wang F, Xu H, Bian J (2024) A scoping review of fair machine learning techniques when using real-world data. J Biomed Inform 151:104622","journal-title":"J Biomed Inform"},{"key":"11246_CR113","doi-asserted-by":"crossref","unstructured":"He K, Gkioxari G, Doll\u00e1r P, Girshick R (2017) Mask r-cnn. In: Proceedings of the IEEE international conference on computer vision, pp. 2961\u20132969","DOI":"10.1109\/ICCV.2017.322"},{"key":"11246_CR114","doi-asserted-by":"crossref","unstructured":"Hustinx A, Hellmann F, S\u00fcmer \u00d6, Javanmardi B, Andr\u00e9 E, Krawitz P, Hsieh T-C (2023) Improving deep facial phenotyping for ultra-rare disorder verification using model ensembles. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 5018\u20135028","DOI":"10.1109\/WACV56688.2023.00499"},{"issue":"2","key":"11246_CR116","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2025.e41802","volume":"11","author":"MM Hassan","year":"2025","unstructured":"Hassan MM, Ismail HR (2025) Bayesian deep learning applied to diabetic retinopathy with uncertainty quantification. Heliyon 11(2):e41802","journal-title":"Heliyon"},{"key":"11246_CR117","doi-asserted-by":"crossref","DOI":"10.1002\/9781118548387","volume-title":"Applied logistic regression","author":"DW Hosmer","year":"2013","unstructured":"Hosmer DW, Lemeshow S (2013) Applied logistic regression. John Wiley, Hoboken"},{"issue":"7","key":"11246_CR118","doi-asserted-by":"crossref","first-page":"815","DOI":"10.2337\/diacare.15.7.815","volume":"15","author":"MI Harris","year":"1992","unstructured":"Harris MI, Klein R, Welborn TA, Knuiman MW (1992) Onset of Niddm occurs at least 4\u20137 yr before clinical diagnosis. Diabetes Care 15(7):815\u2013819","journal-title":"Diabetes Care"},{"key":"11246_CR119","unstructured":"Hsieh T, Lesmann H, Hustinx A, Moosa S, Marchi E, MdPC M, Abdelrazek I, Pantel J, Klinkhammer H, Mt H, et al (2024) Gestaltmatcher database-a global reference for facial phenotypic variability in rare human diseases"},{"key":"11246_CR120","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3416029","author":"M Hu","year":"2024","unstructured":"Hu M, Liu L, Wang X, Tang Y, Yang J, An N (2024) Parallel multiscale bridge fusion network for audio-visual automatic depression assessment. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2024.3416029","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"5","key":"11246_CR121","doi-asserted-by":"crossref","first-page":"427","DOI":"10.1212\/WNL.17.5.427","volume":"17","author":"M Hoehn","year":"1967","unstructured":"Hoehn M, MD Y, (1967) Parkinsonismo: Aparici\u00f3n, progresi\u00f3n y mortalidad. Neurology 17(5):427\u2013442","journal-title":"Neurology"},{"key":"11246_CR122","doi-asserted-by":"crossref","unstructured":"Haley GM, Manjunath B (1995) Rotation-invariant texture classification using modified gabor filters. In: Proceedings., International Conference on Image Processing, vol. 1, pp. 262\u2013265. IEEE","DOI":"10.1109\/ICIP.1995.529696"},{"key":"11246_CR123","volume-title":"Long short-term memory","author":"S Hochreiter","year":"1997","unstructured":"Hochreiter S (1997) Long short-term memory. Neural Computation MIT-Press, Cambridge"},{"issue":"3","key":"11246_CR124","doi-asserted-by":"crossref","first-page":"329","DOI":"10.1001\/jamadermatol.2017.5798","volume":"154","author":"LJ Hoenig","year":"2018","unstructured":"Hoenig LJ (2018) The \u201cmoon face\u2019\u2019 of cushing syndrome. JAMA Dermatol 154(3):329\u2013329","journal-title":"JAMA Dermatol"},{"key":"11246_CR125","doi-asserted-by":"crossref","unstructured":"Hernandez-Ortega J, Galbally J, Fierrez J, Haraksim R, Beslay L (2019) Faceqnet: Quality assessment for face recognition based on deep learning. In: 2019 International Conference on Biometrics (ICB), pp. 1\u20138. IEEE","DOI":"10.1109\/ICB45273.2019.8987255"},{"issue":"10","key":"11246_CR126","doi-asserted-by":"crossref","first-page":"1172","DOI":"10.1016\/j.jcms.2024.02.010","volume":"52","author":"Q Hennocq","year":"2024","unstructured":"Hennocq Q, Paternoster G, Collet C, Amiel J, Bongibault T, Bouygues T, Cormier-Daire V, Douillet M, Dunaway DJ, Jeelani NO et al (2024) Ai-based diagnosis and phenotype-genotype correlations in syndromic craniosynostoses. J Cranio-Maxillofacial Surg 52(10):1172\u20131187","journal-title":"J Cranio-Maxillofacial Surg"},{"issue":"1","key":"11246_CR127","first-page":"514","volume":"33","author":"A Hamosh","year":"2005","unstructured":"Hamosh A, Scott AF, Amberger JS, Bocchini CA, McKusick VA (2005) Online mendelian inheritance in man (omim), a knowledgebase of human genes and genetic disorders. Nucleic Acids Res 33(1):514\u2013517","journal-title":"Nucleic Acids Res"},{"key":"11246_CR128","doi-asserted-by":"crossref","unstructured":"Hu J, Shen L, Sun G (2018) Squeeze-and-excitation networks. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 7132\u20137141","DOI":"10.1109\/CVPR.2018.00745"},{"key":"11246_CR129","volume":"184","author":"J-C Hou","year":"2022","unstructured":"Hou J-C, Thonnat M, Bartolomei F, McGonigal A (2022) Automated video analysis of emotion and dystonia in epileptic seizures. Epilepsy Res 184:106953","journal-title":"Epilepsy Res"},{"issue":"3","key":"11246_CR130","doi-asserted-by":"crossref","first-page":"542","DOI":"10.1037\/a0016105","volume":"24","author":"AR Hemmesch","year":"2009","unstructured":"Hemmesch AR, Tickle-Degnen L, Zebrowitz LA (2009) The influence of facial masking and sex on older adults\u2019 impressions of individuals with Parkinson\u2019s disease. Psychol Aging 24(3):542","journal-title":"Psychol Aging"},{"key":"11246_CR131","doi-asserted-by":"crossref","first-page":"120","DOI":"10.1016\/j.neunet.2022.05.025","volume":"153","author":"L He","year":"2022","unstructured":"He L, Tiwari P, Lv C, Wu W, Guo L (2022) Reducing noisy annotations for depression estimation from facial images. Neural Netw 153:120\u2013129","journal-title":"Neural Netw"},{"key":"11246_CR132","doi-asserted-by":"crossref","unstructured":"Huang L, Wang M, Liang J, Deng W, Shi H, Wen D, Zhang Y, Zhao J (2023) Gradient attention balance network: Mitigating face recognition racial bias via gradient attention. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 38\u201347","DOI":"10.1109\/CVPRW59228.2023.00009"},{"key":"11246_CR133","doi-asserted-by":"publisher","DOI":"10.1109\/JBHI.2023.3239780","author":"W Huang","year":"2023","unstructured":"Huang W, Xu W, Wan R, Zhang P, Zha Y, Pang M (2023) Auto diagnosis of Parkinson\u2019s disease via a deep learning model based on mixed emotional facial expressions. IEEE J Biomed Health Inform. https:\/\/doi.org\/10.1109\/JBHI.2023.3239780","journal-title":"IEEE J Biomed Health Inform"},{"key":"11246_CR134","doi-asserted-by":"crossref","unstructured":"Hu C, Zhang P, Huang W (2021) A novel face-based approach for the early diagnosis of parkinson\u2019s disease. In: 2021 International Conference on Information Technology and Biomedical Engineering (ICITBE), pp. 248\u2013252. IEEE","DOI":"10.1109\/ICITBE54178.2021.00061"},{"key":"11246_CR135","doi-asserted-by":"crossref","DOI":"10.1016\/j.ajp.2022.103263","volume":"77","author":"J Huang","year":"2022","unstructured":"Huang J, Zhao Y, Qu W, Tian Z, Tan Y, Wang Z, Tan S (2022) Automatic recognition of schizophrenia from facial videos using 3d convolutional neural network. Asian J Psychiatry 77:103263","journal-title":"Asian J Psychiatry"},{"key":"11246_CR136","doi-asserted-by":"crossref","unstructured":"He K, Zhang X, Ren S, Sun J (2016) Deep residual learning for image recognition. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 770\u2013778","DOI":"10.1109\/CVPR.2016.90"},{"issue":"11","key":"11246_CR137","doi-asserted-by":"crossref","first-page":"29554","DOI":"10.2196\/29554","volume":"23","author":"X Hou","year":"2021","unstructured":"Hou X, Zhang Y, Wang Y, Wang X, Zhao J, Zhu X, Su J (2021) A markerless 2d video, facial feature recognition-based, artificial intelligence model to assist with screening for parkinson disease: development and usability study. J Med Internet Res 23(11):29554","journal-title":"J Med Internet Res"},{"key":"11246_CR138","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13023-021-01979-y","volume":"16","author":"D Hong","year":"2021","unstructured":"Hong D, Zheng Y-Y, Xin Y, Sun L, Yang H, Lin M-Y, Liu C, Li B-N, Zhang Z-W, Zhuang J et al (2021) Genetic syndromes screening by facial recognition technology: Vgg-16 screening model construction and evaluation. Orphanet J Rare Dis 16:1\u20138","journal-title":"Orphanet J Rare Dis"},{"key":"11246_CR139","unstructured":"Izmailov P, Vikram S, Hoffman MD, Wilson AGG (2021) What are bayesian neural network posteriors really like? In: International Conference on Machine Learning, pp. 4629\u20134640. PMLR"},{"issue":"1","key":"11246_CR140","doi-asserted-by":"crossref","first-page":"6","DOI":"10.1038\/s41746-023-00751-9","volume":"6","author":"DW Joyce","year":"2023","unstructured":"Joyce DW, Kormilitzin A, Smith KA, Cipriani A (2023) Explainable artificial intelligence for mental health through transparency and interpretability for understandability. npj Dig Med 6(1):6","journal-title":"npj Dig Med"},{"key":"11246_CR141","doi-asserted-by":"crossref","unstructured":"Jung J, Kang C, Yoon J, Kim S, Han J (2024) Hique: Hierarchical question embedding network for multimodal depression detection. In: Proceedings of the 33rd ACM International Conference on Information and Knowledge Management, pp. 1049\u20131059","DOI":"10.1145\/3627673.3679797"},{"key":"11246_CR142","doi-asserted-by":"crossref","unstructured":"Jin W, Li X, Hamarneh G (2022) Evaluating explainable ai on a multi-modal medical imaging task: Can existing algorithms fulfill clinical requirements? In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, pp. 11945\u201311953","DOI":"10.1609\/aaai.v36i11.21452"},{"key":"11246_CR143","doi-asserted-by":"crossref","unstructured":"Ju Y-J, Lee G-H, Hong J-H, Lee S-W (2022) Complete face recovery gan: Unsupervised joint face rotation and de-occlusion from a single-view image. In: Proceedings of the IEEE\/CVF Winter Conference on Applications of Computer Vision, pp. 3711\u20133721","DOI":"10.1109\/WACV51458.2022.00124"},{"issue":"7","key":"11246_CR144","doi-asserted-by":"crossref","first-page":"18697","DOI":"10.2196\/18697","volume":"22","author":"B Jin","year":"2020","unstructured":"Jin B, Qu Y, Zhang L, Gao Z (2020) Diagnosing Parkinson disease through facial expression recognition: video analysis. J Med Internet Res 22(7):18697","journal-title":"J Med Internet Res"},{"key":"11246_CR145","doi-asserted-by":"crossref","first-page":"5875","DOI":"10.1109\/TIP.2021.3089943","volume":"30","author":"P-T Jiang","year":"2021","unstructured":"Jiang P-T, Zhang C-B, Hou Q, Cheng M-M, Wei Y (2021) Layercam: exploring hierarchical class activation maps for localization. IEEE Trans Image Proc 30:5875\u20135888","journal-title":"IEEE Trans Image Proc"},{"issue":"1","key":"11246_CR146","doi-asserted-by":"crossref","first-page":"76","DOI":"10.1038\/s41398-024-02802-5","volume":"14","author":"JC Koehler","year":"2024","unstructured":"Koehler JC, Dong MS, Bierlich AM, Fischer S, Sp\u00e4th J, Plank IS, Koutsouleris N, Falter-Wagner CM (2024) Machine learning classification of autism spectrum disorder based on reciprocity in naturalistic social interactions. Trans Psychiatry 14(1):76","journal-title":"Trans Psychiatry"},{"issue":"1","key":"11246_CR147","doi-asserted-by":"crossref","first-page":"131","DOI":"10.1016\/j.ridd.2009.08.010","volume":"31","author":"A Kirby","year":"2010","unstructured":"Kirby A, Edwards L, Sugden D, Rosenblum S (2010) The development and standardization of the adult developmental co-ordination disorders\/dyspraxia checklist (adc). Res Dev Disabil 31(1):131\u2013139","journal-title":"Res Dev Disabil"},{"issue":"2","key":"11246_CR148","doi-asserted-by":"crossref","first-page":"261","DOI":"10.1093\/schbul\/13.2.261","volume":"13","author":"SR Kay","year":"1987","unstructured":"Kay SR, Fiszbein A, Opler LA (1987) The positive and negative syndrome scale (panss) for schizophrenia. Schizophr Bull 13(2):261\u2013276","journal-title":"Schizophr Bull"},{"key":"11246_CR149","doi-asserted-by":"crossref","first-page":"487","DOI":"10.1007\/BF00917077","volume":"11","author":"SH Klee","year":"1983","unstructured":"Klee SH, Garfinkel BD (1983) The computerized continuous performance task: a new measure of inattention. J Abnormal Child Psychol 11:487\u2013495","journal-title":"J Abnormal Child Psychol"},{"issue":"D1","key":"11246_CR150","doi-asserted-by":"crossref","first-page":"1207","DOI":"10.1093\/nar\/gkaa1043","volume":"49","author":"S K\u00f6hler","year":"2021","unstructured":"K\u00f6hler S, Gargano M, Matentzoglu N, Carmody LC, Lewis-Smith D, Vasilevsky NA, Danis D, Balagura G, Baynam G, Brower AM et al (2021) The human phenotype ontology in 2021. Nucleic Acids Res 49(D1):1207\u20131217","journal-title":"Nucleic Acids Res"},{"key":"11246_CR151","doi-asserted-by":"crossref","first-page":"94","DOI":"10.1016\/j.ebiom.2017.12.015","volume":"27","author":"X Kong","year":"2018","unstructured":"Kong X, Gong S, Su L, Howard N, Kong Y (2018) Automatic detection of acromegaly from facial photographs using machine learning methods. EBioMedicine 27:94\u2013102","journal-title":"EBioMedicine"},{"key":"11246_CR152","doi-asserted-by":"crossref","first-page":"377","DOI":"10.1038\/s41431-025-01787-z","volume":"33","author":"A Kirchhoff","year":"2025","unstructured":"Kirchhoff A, Hustinx A, Javanmardi B, Hsieh T-C, Brand F, Hellmann F, Mertes S, Andr\u00e9 E, Moosa S, Schultz T et al (2025) Gestaltgan: synthetic photorealistic portraits of individuals with rare genetic disorders. Eur J Hum Gen 33:377\u2013382","journal-title":"Eur J Hum Gen"},{"key":"11246_CR153","first-page":"1755","volume":"10","author":"DE King","year":"2009","unstructured":"King DE (2009) Dlib-ml: a machine learning toolkit. J Mach Learn Res 10:1755\u20131758","journal-title":"J Mach Learn Res"},{"issue":"1","key":"11246_CR154","doi-asserted-by":"crossref","first-page":"158","DOI":"10.1093\/ejendo\/lvad005","volume":"188","author":"M Kizilgul","year":"2023","unstructured":"Kizilgul M, Karakis R, Dogan N, Bostan H, Yapici MM, Gul U, Ucan B, Duman E, Duger H, Cakal E et al (2023) Real-time detection of acromegaly from facial images with artificial intelligence. Eur J Endocrinol 188(1):158\u2013165","journal-title":"Eur J Endocrinol"},{"key":"11246_CR155","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s13045-020-00925-y","volume":"13","author":"Y Kong","year":"2020","unstructured":"Kong Y, Kong X, He C, Liu C, Wang L, Su L, Gao J, Guo Q, Cheng R (2020) Constructing an automatic diagnosis and severity-classification model for acromegaly using facial photographs by deep learning. J Hematol Oncol 13:1\u20134","journal-title":"J Hematol Oncol"},{"key":"11246_CR156","doi-asserted-by":"crossref","unstructured":"Karras T, Laine S, Aila T (2019) A style-based generator architecture for generative adversarial networks. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 4401\u20134410","DOI":"10.1109\/CVPR.2019.00453"},{"issue":"1080\/21681163","key":"11246_CR157","first-page":"2422408","volume":"10","author":"G Ke","year":"2024","unstructured":"Ke G, Meng Q, Finley T, Wang T, Chen W, Ma W, Ye Q (2024) Lightgbm: a highly efficient gradient boosting decision tree. Adv Neural Inform Proc Syst 10(1080\/21681163):2422408","journal-title":"Adv Neural Inform Proc Syst"},{"issue":"11","key":"11246_CR158","doi-asserted-by":"crossref","first-page":"3964","DOI":"10.1109\/TPAMI.2020.2992934","volume":"43","author":"I Kobyzev","year":"2020","unstructured":"Kobyzev I, Prince SJ, Brubaker MA (2020) Normalizing flows: an introduction and review of current methods. IEEE Trans Pattern Anal Mach Intel 43(11):3964\u20133979","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"key":"11246_CR159","unstructured":"Kvanchiani K, Petrova E, Efremyan K, Sautin A, Kapitanov A (2023) Easyportrait\u2013face parsing and portrait segmentation dataset. arXiv preprint arXiv:2304.13509"},{"issue":"1","key":"11246_CR160","doi-asserted-by":"crossref","first-page":"119","DOI":"10.1080\/23328940.2015.1131506","volume":"3","author":"GP Kenny","year":"2016","unstructured":"Kenny GP, Sigal RJ, McGinn R (2016) Body temperature regulation in diabetes. Temperature 3(1):119\u2013145","journal-title":"Temperature"},{"issue":"1\u20133","key":"11246_CR161","doi-asserted-by":"crossref","first-page":"163","DOI":"10.1016\/j.jad.2008.06.026","volume":"114","author":"K Kroenke","year":"2009","unstructured":"Kroenke K, Strine TW, Spitzer RL, Williams JB, Berry JT, Mokdad AH (2009) The phq-8 as a measure of current depression in the general population. J Affective Disord 114(1\u20133):163\u2013173","journal-title":"J Affective Disord"},{"issue":"9769","key":"11246_CR162","doi-asserted-by":"crossref","first-page":"942","DOI":"10.1016\/S0140-6736(10)61156-7","volume":"377","author":"MC Kiernan","year":"2011","unstructured":"Kiernan MC, Vucic S, Cheah BC, Turner MR, Eisen A, Hardiman O, Burrell JR, Zoing MC (2011) Amyotrophic lateral sclerosis. The Lancet 377(9769):942\u2013955","journal-title":"The Lancet"},{"key":"11246_CR163","unstructured":"Kipf TN, Welling M (2016) Semi-supervised classification with graph convolutional networks. arXiv preprint arXiv:1609.02907"},{"issue":"5","key":"11246_CR164","doi-asserted-by":"crossref","first-page":"1009","DOI":"10.1093\/schbul\/sbn192","volume":"36","author":"CG Kohler","year":"2010","unstructured":"Kohler CG, Walker JB, Martin EA, Healey KM, Moberg PJ (2010) Facial emotion perception in schizophrenia: a meta-analytic review. Schizophrenia Bull 36(5):1009\u20131019","journal-title":"Schizophrenia Bull"},{"key":"11246_CR165","doi-asserted-by":"crossref","unstructured":"Liu W, Anguelov D, Erhan D, Szegedy C, Reed S, Fu C-Y, Berg AC (2016) Ssd: Single shot multibox detector. In: Computer Vision\u2013ECCV 2016: 14th European Conference, Amsterdam, The Netherlands, October 11\u201314, 2016, Proceedings, Part I 14, pp. 21\u201337. Springer","DOI":"10.1007\/978-3-319-46448-0_2"},{"issue":"11","key":"11246_CR166","doi-asserted-by":"crossref","first-page":"2278","DOI":"10.1109\/5.726791","volume":"86","author":"Y LeCun","year":"1998","unstructured":"LeCun Y, Bottou L, Bengio Y, Haffner P (1998) Gradient-based learning applied to document recognition. Proc IEEE 86(11):2278\u20132324","journal-title":"Proc IEEE"},{"issue":"1","key":"11246_CR167","doi-asserted-by":"crossref","first-page":"145","DOI":"10.1038\/s41531-022-00414-8","volume":"8","author":"WS Lim","year":"2022","unstructured":"Lim WS, Chiu S-I, Wu M-C, Tsai S-F, Wang P-H, Lin K-P, Chen Y-M, Peng P-L, Chen Y-Y, Jang J-SR et al (2022) An integrated biometric voice and facial features for early detection of Parkinson\u2019s disease. npj Parkinson\u2019s Dis 8(1):145","journal-title":"npj Parkinson\u2019s Dis"},{"issue":"9996","key":"11246_CR168","doi-asserted-by":"crossref","first-page":"913","DOI":"10.1016\/S0140-6736(14)61375-1","volume":"386","author":"A Lacroix","year":"2015","unstructured":"Lacroix A, Feelders RA, Stratakis CA, Nieman LK (2015) Cushing\u2019s syndrome. The Lancet 386(9996):913\u2013927","journal-title":"The Lancet"},{"issue":"2","key":"11246_CR169","doi-asserted-by":"crossref","first-page":"671","DOI":"10.1109\/TAFFC.2023.3288885","volume":"15","author":"B Liu","year":"2023","unstructured":"Liu B, Guo J, Chen CP, Wu X, Zhang T (2023) Fine-grained interpretability for eeg emotion recognition: Concat-aided grad-cam and systematic brain functional network. IEEE Trans Affective Comput 15(2):671\u2013684","journal-title":"IEEE Trans Affective Comput"},{"key":"11246_CR170","doi-asserted-by":"crossref","unstructured":"Luyster R, Gotham K, Guthrie W, Coffing M, Petrak R, Pierce K, Bishop S, Esler A, Hus V, Oti R et al (2009) The autism diagnostic observation schedule\u00e2\u20ac\u201dtoddler module: A new module of a standardized diagnostic measure for autism spectrum disorders. J Autism Dev Disorders 39:1305\u20131320","DOI":"10.1007\/s10803-009-0746-z"},{"key":"11246_CR171","doi-asserted-by":"crossref","DOI":"10.1016\/j.patcog.2024.111112","volume":"159","author":"J Liao","year":"2025","unstructured":"Liao J, Guha T, Sanchez V (2025) Self-supervised random mask attention gan in tackling pose-invariant face recognition. Pattern Recognit 159:111112","journal-title":"Pattern Recognit"},{"issue":"10","key":"11246_CR172","first-page":"1","volume":"56","author":"H-I Liu","year":"2024","unstructured":"Liu H-I, Galindo M, Xie H, Wong L-K, Shuai H-H, Li Y-H, Cheng W-H (2024) Lightweight deep learning for resource-constrained environments: a survey. ACM Comput Surv 56(10):1\u201342","journal-title":"ACM Comput Surv"},{"key":"11246_CR173","doi-asserted-by":"crossref","first-page":"1226470","DOI":"10.3389\/fpsyg.2023.1226470","volume":"14","author":"Y Li","year":"2023","unstructured":"Li Y, Huang W-C, Song P-H (2023) A face image classification method of autistic children based on the two-phase transfer learning. Front Psychol 14:1226470","journal-title":"Front Psychol"},{"key":"11246_CR174","doi-asserted-by":"crossref","unstructured":"Liu Z, Lin Y, Cao Y, Hu H, Wei Y, Zhang Z, Lin S, Guo B (2021) Swin transformer: Hierarchical vision transformer using shifted windows. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 10012\u201310022","DOI":"10.1109\/ICCV48922.2021.00986"},{"key":"11246_CR175","doi-asserted-by":"crossref","first-page":"1017064","DOI":"10.3389\/fpsyt.2022.1017064","volume":"13","author":"D Liu","year":"2022","unstructured":"Liu D, Liu B, Lin T, Liu G, Yang G, Qi D, Qiu Y, Lu Y, Yuan Q, Shuai SC et al (2022) Measuring depression severity based on facial expression and body movement using deep convolutional neural network. Front Psychiatry 13:1017064","journal-title":"Front Psychiatry"},{"issue":"8","key":"11246_CR176","doi-asserted-by":"crossref","first-page":"888","DOI":"10.1002\/aur.1615","volume":"9","author":"W Liu","year":"2016","unstructured":"Liu W, Li M, Yi L (2016) Identifying children with autism spectrum disorder based on their face processing abnormality: a machine learning framework. Autism Res 9(8):888\u2013898","journal-title":"Autism Res"},{"issue":"1","key":"11246_CR177","doi-asserted-by":"crossref","first-page":"6261","DOI":"10.1038\/s41467-023-41974-4","volume":"14","author":"M Lin","year":"2023","unstructured":"Lin M, Li T, Yang Y, Holste G, Ding Y, Van Tassel SH, Kovacs K, Shih G, Wang Z, Lu Z et al (2023) Improving model fairness in image-based computer-aided diagnosis. Nat Commun 14(1):6261","journal-title":"Nat Commun"},{"key":"11246_CR178","volume":"9","author":"H Liu","year":"2021","unstructured":"Liu H, Mo Z-H, Yang H, Zhang Z-F, Hong D, Wen L, Lin M-Y, Zheng Y-Y, Zhang Z-W, Xu X-W et al (2021) Automatic facial recognition of Williams-Beuren syndrome based on deep convolutional neural networks. Front Pediatrics 9:648255","journal-title":"Front Pediatrics"},{"issue":"1","key":"11246_CR179","doi-asserted-by":"crossref","first-page":"62","DOI":"10.1186\/s13195-022-00983-z","volume":"14","author":"L Lampe","year":"2022","unstructured":"Lampe L, Niehaus S, Huppertz H-J, Merola A, Reinelt J, Mueller K, Anderl-Straub S, Fassbender K, Fliessbach K, Jahn H et al (2022) Comparative analysis of machine learning algorithms for multi-syndrome classification of neurodegenerative syndromes. Alzheimer\u2019s Res Ther 14(1):62","journal-title":"Alzheimer\u2019s Res Ther"},{"issue":"15","key":"11246_CR180","doi-asserted-by":"crossref","first-page":"1451","DOI":"10.1056\/NEJMra1505550","volume":"376","author":"DL Loriaux","year":"2017","unstructured":"Loriaux DL (2017) Diagnosis and differential diagnosis of Cushing\u2019s syndrome. New England J Med 376(15):1451\u20131459","journal-title":"New England J Med"},{"key":"11246_CR181","unstructured":"Lakshminarayanan B, Pritzel A, Blundell C (2017) Simple and scalable predictive uncertainty estimation using deep ensembles. Adv Neural Inform Proc Syst 30"},{"key":"11246_CR182","doi-asserted-by":"crossref","first-page":"156","DOI":"10.1016\/j.ymeth.2024.07.001","volume":"229","author":"H Li","year":"2024","unstructured":"Li H, Su D, Zhang X, He Y, Luo X, Xiong Y, Zou M, Wei H, Wen S, Xi Q et al (2024) Machine learning-based prediction of diabetic patients using blood routine data. Methods 229:156\u2013162","journal-title":"Methods"},{"issue":"5","key":"11246_CR183","doi-asserted-by":"crossref","first-page":"335","DOI":"10.1111\/j.1600-0404.1995.tb07018.x","volume":"91","author":"S Lehrl","year":"1995","unstructured":"Lehrl S, Triebig G, Fischer B (1995) Multiple choice vocabulary test mwt as a valid and short test to estimate premorbid intelligence. Acta Neurologica Scandinavica 91(5):335\u2013345","journal-title":"Acta Neurologica Scandinavica"},{"key":"11246_CR184","doi-asserted-by":"crossref","unstructured":"Liao Y, Thompson C, Peterson S, Mandrola J, Beg MS (2019) The future of wearable technologies and remote monitoring in health care. In: American Society of Clinical Oncology Educational Book. American Society of Clinical Oncology. Annual Meeting, vol. 39, p. 115. NIH Public Access","DOI":"10.1200\/EDBK_238919"},{"key":"11246_CR185","doi-asserted-by":"publisher","DOI":"10.1109\/TCSS.2024.3393247","author":"M Li","year":"2024","unstructured":"Li M, Wang Y, Yang C, Lu Z, Chen J (2024) Automatic diagnosis of depression based on facial expression information and deep convolutional neural network. IEEE Trans Comput Soc Syst. https:\/\/doi.org\/10.1109\/TCSS.2024.3393247","journal-title":"IEEE Trans Comput Soc Syst"},{"issue":"3","key":"11246_CR186","doi-asserted-by":"crossref","first-page":"1128","DOI":"10.1109\/JBHI.2021.3097735","volume":"26","author":"X Liu","year":"2021","unstructured":"Liu X, Xing F, Yang C, Kuo C-CJ, Babu S, El Fakhri G, Jenkins T, Woo J (2021) Voxelhop: successive subspace learning for ALS disease classification using structural MRI. IEEE J Biomed Health Inform 26(3):1128\u20131139","journal-title":"IEEE J Biomed Health Inform"},{"key":"11246_CR187","volume":"157","author":"Z Liu","year":"2023","unstructured":"Liu Z, Yuan X, Li Y, Shangguan Z, Zhou L, Hu B (2023) Pra-net: Part-and-relation attention network for depression recognition from facial expression. Comput Biol Med 157:106589","journal-title":"Comput Biol Med"},{"key":"11246_CR188","unstructured":"MacQueen J (1967) Some methods for classification and analysis of multivariate observations. In: Proceedings of 5-th Berkeley Symposium on Mathematical Statistics and Probability\/University of California Press"},{"issue":"11","key":"11246_CR189","first-page":"1085","volume":"18","author":"M Muenke","year":"2016","unstructured":"Muenke M, Adeyemo A, Kruszka P (2016) An electronic atlas of human malformation syndromes in diverse populations. Gen Med 18(11):1085\u20131087","journal-title":"Gen Med"},{"key":"11246_CR190","doi-asserted-by":"crossref","DOI":"10.1016\/j.cosrev.2023.100546","volume":"48","author":"H Murtaza","year":"2023","unstructured":"Murtaza H, Ahmed M, Khan NF, Murtaza G, Zafar S, Bano A (2023) Synthetic data generation: state of the art in health care domain. Comput Sci Rev 48:100546","journal-title":"Comput Sci Rev"},{"issue":"1","key":"11246_CR191","doi-asserted-by":"crossref","first-page":"196","DOI":"10.1186\/s13063-023-07165-8","volume":"24","author":"M Mayhew","year":"2023","unstructured":"Mayhew M, Balderson BH, Cook AJ, Dickerson JF, Elder CR, Firemark AJ, Haller IV, Justice M, Keefe FJ, McMullen CK et al (2023) Comparing the clinical and cost-effectiveness of remote (telehealth and online) cognitive behavioral therapy-based treatments for high-impact chronic pain relative to usual care: study protocol for the resolve multisite randomized control trial. Trials 24(1):196","journal-title":"Trials"},{"issue":"4","key":"11246_CR192","doi-asserted-by":"crossref","first-page":"869","DOI":"10.1016\/j.ecl.2013.07.004","volume":"42","author":"B Murphy-Chutorian","year":"2013","unstructured":"Murphy-Chutorian B, Han G, Cohen SR (2013) Dermatologic manifestations of diabetes mellitus: a review. Endocrinol Metabolism Clinics 42(4):869\u2013898","journal-title":"Endocrinol Metabolism Clinics"},{"key":"11246_CR193","doi-asserted-by":"crossref","first-page":"492","DOI":"10.3389\/fendo.2020.00492","volume":"11","author":"T Meng","year":"2020","unstructured":"Meng T, Guo X, Lian W, Deng K, Gao L, Wang Z, Huang J, Wang X, Long X, Xing B (2020) Identifying facial features and predicting patients of acromegaly using three-dimensional imaging techniques and machine learning. Front Endocrinol 11:492","journal-title":"Front Endocrinol"},{"key":"11246_CR194","volume":"37","author":"E Mseke","year":"2024","unstructured":"Mseke E, Jessup B, Barnett T (2024) Impact of distance and\/or travel time on healthcare service access in rural and remote areas: a scoping review. J Trans Health 37:101819","journal-title":"J Trans Health"},{"key":"11246_CR195","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2020.105619","volume":"196","author":"T Majtner","year":"2020","unstructured":"Majtner T, Nadimi ES, Yderstr\u00e6de KB, Blanes-Vidal V (2020) Non-invasive detection of diabetic complications via pattern analysis of temporal facial colour variations. Comput Methods Prog Biomed 196:105619","journal-title":"Comput Methods Prog Biomed"},{"key":"11246_CR196","unstructured":"Mirza M, Osindero S (2014) Conditional generative adversarial nets. arXiv preprint arXiv:1411.1784"},{"key":"11246_CR197","unstructured":"Marcinkevics R, Ozkan E, Vogt JE (2022) Debiasing deep chest x-ray classifiers using intra-and post-processing methods. In: Machine Learning for Healthcare Conference, pp. 504\u2013536. PMLR"},{"issue":"1","key":"11246_CR198","doi-asserted-by":"crossref","first-page":"2100545","DOI":"10.1002\/admt.202100545","volume":"7","author":"S Mirjalali","year":"2022","unstructured":"Mirjalali S, Peng S, Fang Z, Wang C-H, Wu S (2022) Wearable sensors for remote health monitoring: potential applications for early diagnosis of covid-19. Adv Mater Technol 7(1):2100545","journal-title":"Adv Mater Technol"},{"issue":"1","key":"11246_CR199","doi-asserted-by":"crossref","first-page":"94","DOI":"10.3390\/brainsci12010094","volume":"12","author":"K Mujeeb Rahman","year":"2022","unstructured":"Mujeeb Rahman K, Subashini MM (2022) Identification of autism in children using static facial features and deep neural networks. Brain Sci 12(1):94","journal-title":"Brain Sci"},{"key":"11246_CR200","doi-asserted-by":"crossref","unstructured":"Morency L-P, Stratou G, DeVault D, Hartholt A, Lhommet M, Lucas G, Morbini F, Georgila K, Scherer S, Gratch J, et al (2015) Simsensei demonstration: a perceptive virtual human interviewer for healthcare applications. In: Proceedings of the AAAI Conference on Artificial Intelligence, vol. 29","DOI":"10.1609\/aaai.v29i1.9777"},{"key":"11246_CR201","doi-asserted-by":"crossref","unstructured":"Ma X, Yang H, Chen Q, Huang D, Wang Y (2016) Depaudionet: An efficient deep model for audio based depression classification. In: Proceedings of the 6th International Workshop on Audio\/visual Emotion Challenge, pp. 35\u201342","DOI":"10.1145\/2988257.2988267"},{"issue":"10","key":"11246_CR202","doi-asserted-by":"crossref","first-page":"1052","DOI":"10.1001\/jama.2015.9536","volume":"314","author":"DM Nathan","year":"2015","unstructured":"Nathan DM (2015) Diabetes: advances in diagnosis and treatment. Jama 314(10):1052\u20131062","journal-title":"Jama"},{"issue":"5","key":"11246_CR203","doi-asserted-by":"crossref","first-page":"1038","DOI":"10.1002\/lary.20868","volume":"120","author":"JG Neely","year":"2010","unstructured":"Neely JG, Cherian NG, Dickerson CB, Nedzelski JM (2010) Sunnybrook facial grading system: reliability and criteria for grading. The Laryngoscope 120(5):1038\u20131045","journal-title":"The Laryngoscope"},{"key":"11246_CR204","volume":"9","author":"N Naik","year":"2022","unstructured":"Naik N, Hameed B, Shetty DK, Swain D, Shah M, Paul R, Aggarwal K, Ibrahim S, Patil V, Smriti K et al (2022) Legal and ethical consideration in artificial intelligence in healthcare: who takes responsibility? Front Surg 9:862322","journal-title":"Front Surg"},{"issue":"1","key":"11246_CR205","doi-asserted-by":"crossref","first-page":"2","DOI":"10.1016\/j.yebeh.2009.02.029","volume":"15","author":"S Noachtar","year":"2009","unstructured":"Noachtar S, Peters AS (2009) Semiology of epileptic seizures: a critical review. Epilepsy Behav 15(1):2\u20139","journal-title":"Epilepsy Behav"},{"issue":"11","key":"11246_CR206","doi-asserted-by":"crossref","first-page":"8065","DOI":"10.1109\/TCSVT.2022.3182658","volume":"32","author":"M Niu","year":"2022","unstructured":"Niu M, Zhao Z, Tao J, Li Y, Schuller BW (2022) Selective element and two orders vectorization networks for automatic depression severity diagnosis via facial changes. IEEE Trans Circuits Syst Video Technol 32(11):8065\u20138077","journal-title":"IEEE Trans Circuits Syst Video Technol"},{"key":"11246_CR207","unstructured":"Organization WH, et al (1999) Definition, diagnosis and classification of diabetes mellitus and its complications: report of a who consultation. part 1, diagnosis and classification of diabetes mellitus. Technical report, World health organization"},{"issue":"1","key":"11246_CR208","doi-asserted-by":"crossref","first-page":"7761","DOI":"10.1038\/s41467-022-34945-8","volume":"13","author":"H Olsson","year":"2022","unstructured":"Olsson H, Kartasalo K, Mulliqi N, Capuccini M, Ruusuvuori P, Samaratunga H, Delahunt B, Lindskog C, Janssen EA, Blilie A et al (2022) Estimating diagnostic uncertainty in artificial intelligence assisted pathology using conformal prediction. Nat Commun 13(1):7761","journal-title":"Nat Commun"},{"key":"11246_CR209","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2023.107713","volume":"240","author":"GC Oliveira","year":"2023","unstructured":"Oliveira GC, Ngo QC, Passos LA, Papa JP, Jodas DS, Kumar D (2023) Tabular data augmentation for video-based detection of hypomimia in Parkinson\u2019s disease. Comput Methods Prog Biomed 240:107713","journal-title":"Comput Methods Prog Biomed"},{"key":"11246_CR210","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2024.108195","volume":"250","author":"GC Oliveira","year":"2024","unstructured":"Oliveira GC, Ngo QC, Passos LA, Oliveira LS, Papa JP, Kumar D (2024) Facial expressions to identify post-stroke: a pilot study. Comput Methods Prog Biomed 250:108195","journal-title":"Comput Methods Prog Biomed"},{"issue":"1","key":"11246_CR211","doi-asserted-by":"crossref","first-page":"171","DOI":"10.1159\/000540547","volume":"8","author":"GC Oliveira","year":"2024","unstructured":"Oliveira GC, Ngo QC, Passos LA, Oliveira LS, Stylianou S, Papa JP, Kumar D (2024) Video assessment to detect amyotrophic lateral sclerosis. Digital Biomarkers 8(1):171\u2013180","journal-title":"Digital Biomarkers"},{"issue":"7","key":"11246_CR212","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1109\/TPAMI.2002.1017623","volume":"24","author":"T Ojala","year":"2002","unstructured":"Ojala T, Pietikainen M, Maenpaa T (2002) Multiresolution gray-scale and rotation invariant texture classification with local binary patterns. IEEE Trans Pattern Anal Mach Intel 24(7):971\u2013987","journal-title":"IEEE Trans Pattern Anal Mach Intel"},{"issue":"7","key":"11246_CR213","doi-asserted-by":"crossref","first-page":"738","DOI":"10.1002\/mds.10473","volume":"18","author":"Parkinson\u2019s Disease MDSTF","year":"2003","unstructured":"Parkinson\u2019s Disease MDSTF (2003) The unified Parkinson\u2019s disease rating scale (updrs): status and recommendations. Mov Disord 18(7):738\u2013750","journal-title":"Mov Disord"},{"key":"11246_CR214","doi-asserted-by":"crossref","DOI":"10.1016\/j.cmpb.2022.107132","volume":"226","author":"A Othmani","year":"2022","unstructured":"Othmani A, Zeghina A-O, Muzammel M (2022) A model of normality inspired deep learning framework for depression relapse prediction using audiovisual data. Comput Methods Prog Biomed 226:107132","journal-title":"Comput Methods Prog Biomed"},{"key":"11246_CR215","unstructured":"Paulus C (2009) Der saarbr\u00fccker pers\u00f6nlichkeitsfragebogen spf (iri) zur messung von empathie: psychometrische evaluation der deutschen version des interpersonal reactivity index (2009)"},{"key":"11246_CR216","doi-asserted-by":"crossref","unstructured":"Perotti A, Borile C, Miola A, Nerini FP, Baracco P, Panisson A (2024) Explainability, quantified: Benchmarking xai techniques. In: World Conference on Explainable Artificial Intelligence, pp. 421\u2013444. Springer","DOI":"10.1007\/978-3-031-63787-2_22"},{"issue":"1","key":"11246_CR217","doi-asserted-by":"crossref","first-page":"195","DOI":"10.1038\/s41746-024-01190-w","volume":"7","author":"A Mortanges","year":"2024","unstructured":"Mortanges A, Luo H, Shu SZ, Kamath A, Suter Y, Shelan M, P\u00f6llinger A, Reyes M (2024) Orchestrating explainable artificial intelligence for multimodal and longitudinal data in medical imaging. NPJ Digital Med 7(1):195","journal-title":"NPJ Digital Med"},{"issue":"10","key":"11246_CR218","doi-asserted-by":"crossref","first-page":"505","DOI":"10.1530\/ERC-17-0253","volume":"24","author":"P Petrossians","year":"2017","unstructured":"Petrossians P, Daly AF, Natchev E, Maione L, Blijdorp K, Sahnoun-Fathallah M, Auriemma R, Diallo AM, Hulting A-L, Ferone D et al (2017) Acromegaly at diagnosis in 3173 patients from the li\u00e8ge acromegaly survey (las) database. Endocrine-Related Cancer 24(10):505","journal-title":"Endocrine-Related Cancer"},{"issue":"10","key":"11246_CR219","doi-asserted-by":"crossref","first-page":"685","DOI":"10.1055\/a-0887-4233","volume":"127","author":"KH Popp","year":"2019","unstructured":"Popp KH, Kosilek RP, Frohner R, Stalla GK, Athanasoulia-Kaspar A, Berr C, Zopp S, Reincke M, Witt M, W\u00fcrtz RP et al (2019) Computer vision technology in the differential diagnosis of Cushing\u2019s syndrome. Exp Clin Endocrinol Diabetes 127(10):685\u2013690","journal-title":"Exp Clin Endocrinol Diabetes"},{"key":"11246_CR220","doi-asserted-by":"crossref","first-page":"1507448","DOI":"10.3389\/fbinf.2025.1507448","volume":"5","author":"C Papangelou","year":"2025","unstructured":"Papangelou C, Kyriakidis K, Natsiavas P, Chouvarda I, Malousi A (2025) Reliable machine learning models in genomic medicine using conformal prediction. Front Bioinformat 5:1507448","journal-title":"Front Bioinformat"},{"issue":"7","key":"11246_CR221","doi-asserted-by":"crossref","DOI":"10.1016\/j.patter.2024.100974","volume":"5","author":"S Pati","year":"2024","unstructured":"Pati S, Kumar S, Varma A, Edwards B, Lu C, Qu L, Wang JJ, Lakshminarayanan A, Wang S-h, Sheller MJ et al (2024) Privacy preservation for federated learning in health care. Patterns 5(7):100974","journal-title":"Patterns"},{"issue":"1","key":"11246_CR222","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1093\/bioinformatics\/btae239","volume":"40","author":"T Patel","year":"2024","unstructured":"Patel T, Othman AA, S\u00fcmer \u00d6, Hellman F, Krawitz P, Andr\u00e9 E, Ripper ME, Fortney C, Persky S, Hu P et al (2024) Approximating facial expression effects on diagnostic accuracy via generative ai in medical genetics. Bioinformatics 40(1):110\u2013118","journal-title":"Bioinformatics"},{"issue":"10","key":"11246_CR223","doi-asserted-by":"crossref","first-page":"635","DOI":"10.1016\/S2589-7500(21)00137-0","volume":"3","author":"AR Porras","year":"2021","unstructured":"Porras AR, Rosenbaum K, Tor-Diez C, Summar M, Linguraru MG (2021) Development and evaluation of a machine learning-based point-of-care screening tool for genetic syndromes in children: a multinational retrospective study. Lancet Digital Health 3(10):635\u2013643","journal-title":"Lancet Digital Health"},{"issue":"2","key":"11246_CR224","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3439950","volume":"54","author":"G Pang","year":"2021","unstructured":"Pang G, Shen C, Cao L, Hengel AVD (2021) Deep learning for anomaly detection: a review. ACM Comput Surv (CSUR) 54(2):1\u201338","journal-title":"ACM Comput Surv (CSUR)"},{"key":"11246_CR225","doi-asserted-by":"crossref","unstructured":"Paproki A, Salvado O, Fookes C (2024) Synthetic data for deep learning in computer vision & medical imaging: A means to reduce data bias. ACM Computing Surveys","DOI":"10.1145\/3663759"},{"key":"11246_CR226","doi-asserted-by":"crossref","first-page":"865","DOI":"10.1007\/s12020-020-02539-3","volume":"72","author":"Z Pan","year":"2021","unstructured":"Pan Z, Shen Z, Zhu H, Bao Y, Liang S, Wang S, Li X, Niu L, Dong X, Shang X et al (2021) Clinical application of an automatic facial recognition system based on deep learning for diagnosis of turner syndrome. Endocrine 72:865\u2013873","journal-title":"Endocrine"},{"key":"11246_CR227","first-page":"81","volume":"1","author":"JR Quinlan","year":"1986","unstructured":"Quinlan JR (1986) Induction of decision trees. Mach Learn 1:81\u2013106","journal-title":"Mach Learn"},{"issue":"7","key":"11246_CR228","doi-asserted-by":"crossref","first-page":"273","DOI":"10.3390\/bioengineering9070273","volume":"9","author":"J Qiang","year":"2022","unstructured":"Qiang J, Wu D, Du H, Zhu H, Chen S, Pan H (2022) Review on facial-recognition-based applications in disease diagnosis. Bioengineering 9(7):273","journal-title":"Bioengineering"},{"issue":"2","key":"11246_CR229","doi-asserted-by":"crossref","first-page":"154","DOI":"10.1016\/j.otohns.2008.11.021","volume":"140","author":"SD Reitzen","year":"2009","unstructured":"Reitzen SD, Babb JS, Lalwani AK (2009) Significance and reliability of the house-Brackmann grading system for regional facial nerve function. Otolaryngol Head Neck Surg 140(2):154\u2013158","journal-title":"Otolaryngol Head Neck Surg"},{"issue":"1","key":"11246_CR230","doi-asserted-by":"crossref","first-page":"31","DOI":"10.1038\/s41591-021-01614-0","volume":"28","author":"P Rajpurkar","year":"2022","unstructured":"Rajpurkar P, Chen E, Banerjee O, Topol EJ (2022) Ai in health and medicine. Nat Med 28(1):31\u201338","journal-title":"Nat Med"},{"issue":"7","key":"11246_CR231","doi-asserted-by":"crossref","first-page":"1641","DOI":"10.1002\/ajmg.a.33441","volume":"152","author":"S Rohatgi","year":"2010","unstructured":"Rohatgi S, Clark D, Kline AD, Jackson LG, Pie J, Siu V, Ramos FJ, Krantz ID, Deardorff MA (2010) Facial diagnosis of mild and variant CDLS: insights from a dysmorphologist survey. Am J Med Gen Part A 152(7):1641\u20131653","journal-title":"Am J Med Gen Part A"},{"issue":"1","key":"11246_CR232","first-page":"198","volume":"59","author":"P Reddy","year":"2024","unstructured":"Reddy P (2024) Diagnosis of autism in children using deep learning techniques by analyzing facial features. Eng Proc 59(1):198","journal-title":"Eng Proc"},{"key":"11246_CR233","doi-asserted-by":"crossref","unstructured":"Ronneberger O, Fischer P, Brox T (2015) U-net: Convolutional networks for biomedical image segmentation. In: Medical Image Computing and Computer-assisted intervention\u2013MICCAI 2015: 18th International Conference, Munich, Germany, October 5-9, 2015, Proceedings, Part III 18, pp. 234\u2013241. Springer","DOI":"10.1007\/978-3-319-24574-4_28"},{"issue":"5","key":"11246_CR234","doi-asserted-by":"crossref","first-page":"756","DOI":"10.1109\/JPROC.2021.3052449","volume":"109","author":"L Ruff","year":"2021","unstructured":"Ruff L, Kauffmann JR, Vandermeulen RA, Montavon G, Samek W, Kloft M, Dietterich TG, M\u00fcller K-R (2021) A unifying review of deep and shallow anomaly detection. Proc IEEE 109(5):756\u2013795","journal-title":"Proc IEEE"},{"issue":"2","key":"11246_CR235","doi-asserted-by":"crossref","first-page":"254","DOI":"10.3390\/diagnostics13020254","volume":"13","author":"EA Rodr\u00edguez Mart\u00ednez","year":"2023","unstructured":"Rodr\u00edguez Mart\u00ednez EA, Polezhaeva O, Marcellin F, Colin \u00c9, Boyaval L, Sarhan F-R, Dakp\u00e9 S (2023) Deepsmile: anomaly detection software for facial movement assessment. Diagnostics 13(2):254","journal-title":"Diagnostics"},{"key":"11246_CR236","doi-asserted-by":"crossref","unstructured":"Rawal A, McCoy J, Raglin A, Rawat DB (2023) A quantitative comparison of causality and feature relevance via explainable ai (xai) for robust, and trustworthy artificial reasoning systems. In: International Conference on Human-Computer Interaction, pp. 274\u2013285. Springer","DOI":"10.1007\/978-3-031-35891-3_17"},{"issue":"3","key":"11246_CR237","doi-asserted-by":"crossref","first-page":"12","DOI":"10.1210\/clinem\/dgz136","volume":"105","author":"G Rubinstein","year":"2020","unstructured":"Rubinstein G, Osswald A, Hoster E, Losa M, Elenkova A, Zacharieva S, Machado MC, Hanzu FA, Zopp S, Ritzel K et al (2020) Time to diagnosis in Cushing\u2019s syndrome: a meta-analysis based on 5367 patients. J Clin Endocrinol Metabolism 105(3):12\u201322","journal-title":"J Clin Endocrinol Metabolism"},{"key":"11246_CR238","unstructured":"Ramey JA, Stein CK, Young PD, Young DM (2016) High-dimensional regularized discriminant analysis. arXiv preprint arXiv:1602.01182"},{"issue":"5","key":"11246_CR239","doi-asserted-by":"crossref","first-page":"206","DOI":"10.1038\/s42256-019-0048-x","volume":"1","author":"C Rudin","year":"2019","unstructured":"Rudin C (2019) Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead. Nat Mach Intel 1(5):206\u2013215","journal-title":"Nat Mach Intel"},{"issue":"18","key":"11246_CR240","doi-asserted-by":"crossref","DOI":"10.1242\/dev.191213","volume":"147","author":"BD Samuels","year":"2020","unstructured":"Samuels BD, Aho R, Brinkley JF, Bugacov A, Feingold E, Fisher S, Gonzalez-Reiche AS, Hacia JG, Hallgrimsson B, Hansen K et al (2020) Facebase 3: analytical tools and fair resources for craniofacial and dental research. Development 147(18):191213","journal-title":"Development"},{"issue":"1","key":"11246_CR241","first-page":"85","volume":"22","author":"K Schwarze","year":"2020","unstructured":"Schwarze K, Buchanan J, Fermont JM, Dreau H, Tilley MW, Taylor JM, Antoniou P, Knight SJ, Camps C, Pentony MM et al (2020) The complete costs of genome sequencing: a microcosting study in cancer and rare diseases from a single center in the united kingdom. Gen Med 22(1):85\u201394","journal-title":"Gen Med"},{"issue":"16","key":"11246_CR242","doi-asserted-by":"crossref","first-page":"1229","DOI":"10.1016\/j.jpsychires.2010.04.024","volume":"44","author":"KL Schaefer","year":"2010","unstructured":"Schaefer KL, Baumann J, Rich BA, Luckenbaugh DA, Zarate CA Jr (2010) Perception of facial emotion in adults with bipolar or unipolar depression and controls. J Psychiatric Res 44(16):1229\u20131235","journal-title":"J Psychiatric Res"},{"issue":"3","key":"11246_CR243","doi-asserted-by":"crossref","first-page":"486","DOI":"10.1007\/s11102-022-01216-0","volume":"25","author":"C Sulu","year":"2022","unstructured":"Sulu C, Bekta\u015f AB, \u015eahin S, Durcan E, Kara Z, Demir AN, \u00d6zkaya HM, Tanr\u0131\u00f6ver N, \u00c7omuno\u011flu N, K\u0131z\u0131lk\u0131l\u0131\u00e7 O et al (2022) Machine learning as a clinical decision support tool for patients with acromegaly. Pituitary 25(3):486\u2013495","journal-title":"Pituitary"},{"key":"11246_CR244","doi-asserted-by":"crossref","unstructured":"Selvaraju RR, Cogswell M, Das A, Vedantam R, Parikh D, Batra D (2017) Grad-cam: Visual explanations from deep networks via gradient-based localization. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 618\u2013626","DOI":"10.1109\/ICCV.2017.74"},{"key":"11246_CR245","doi-asserted-by":"publisher","DOI":"10.1093\/postmj\/qgae095","author":"J-J Shen","year":"2024","unstructured":"Shen J-J, Chen Q-C, Huang Y-L, Wu K, Yang L-C, Wang S-S (2024) Facial recognition models for identifying genetic syndromes associated with pulmonary stenosis in children. Postgrad Med J. https:\/\/doi.org\/10.1093\/postmj\/qgae095","journal-title":"Postgrad Med J"},{"key":"11246_CR246","doi-asserted-by":"crossref","unstructured":"Segal DL (2010) Diagnostic and statistical manual of mental disorders (dsm-iv-tr). Corsini Encyclopedia Psychol, 1\u20133","DOI":"10.1002\/9780470479216.corpsy0271"},{"issue":"1","key":"11246_CR247","doi-asserted-by":"crossref","first-page":"12763","DOI":"10.1038\/s41598-024-63478-x","volume":"14","author":"H Shi","year":"2024","unstructured":"Shi H, Fan Y, Zhang Y, Li X, Shu Y, Deng X, Zhang Y, Zheng Y, Yang J (2024) Intelligent bell facial paralysis assessment: a facial recognition model using improved ssd network. Sci Rep 14(1):12763","journal-title":"Sci Rep"},{"key":"11246_CR248","doi-asserted-by":"crossref","first-page":"312","DOI":"10.3389\/fpsyt.2018.00312","volume":"9","author":"J Sevos","year":"2018","unstructured":"Sevos J, Grosselin A, Gauthier M, Carmona F, Gay A, Massoubre C (2018) Cinemotion, a program of cognitive remediation to improve the recognition and expression of facial emotions in schizophrenia: A pilot study. Front Psychiatry 9:312","journal-title":"Front Psychiatry"},{"issue":"3","key":"11246_CR249","doi-asserted-by":"crossref","first-page":"324","DOI":"10.1037\/1040-3590.17.3.324","volume":"17","author":"A Shafer","year":"2005","unstructured":"Shafer A (2005) Meta-analysis of the brief psychiatric rating scale factor structure. Psychol Asses 17(3):324","journal-title":"Psychol Asses"},{"key":"11246_CR250","unstructured":"Song J, He M, Feng J, Shen B (2024) Bridging the gaps: Utilizing unlabeled face recognition datasets to boost semi-supervised facial expression recognition. arXiv preprint arXiv:2410.17622"},{"key":"11246_CR251","doi-asserted-by":"crossref","unstructured":"S\u00fcmer \u00d6, Hellmann F, Hustinx A, Hsieh T-C, Andr\u00e9 E, Krawitz P (2023) Few-shot meta-learning for recognizing facial phenotypes of genetic disorders. In: Caring Is Sharing\u2013Exploiting the Value in Data for Health and Innovation, pp. 932\u2013936. IOS Press","DOI":"10.3233\/SHTI230312"},{"key":"11246_CR316","doi-asserted-by":"publisher","unstructured":"Song J, He M, Ren S et al (2025) An explainable dataset linking facial phenotypes and genes to rare genetic diseases. Sci Data 12:634. https:\/\/doi.org\/10.1038\/s41597-025-04922-z","DOI":"10.1038\/s41597-025-04922-z"},{"issue":"1","key":"11246_CR253","doi-asserted-by":"crossref","first-page":"110","DOI":"10.1038\/s43856-024-00528-5","volume":"4","author":"J Sahlsten","year":"2024","unstructured":"Sahlsten J, Jaskari J, Wahid KA, Ahmed S, Glerean E, He R, Kann BH, M\u00e4kitie A, Fuller CD, Naser MA et al (2024) Application of simultaneous uncertainty quantification and segmentation for oropharyngeal cancer use-case with bayesian deep learning. Commun Med 4(1):110","journal-title":"Commun Med"},{"issue":"11","key":"11246_CR254","doi-asserted-by":"crossref","first-page":"8135","DOI":"10.1109\/TNNLS.2022.3152527","volume":"34","author":"H Song","year":"2022","unstructured":"Song H, Kim M, Park D, Shin Y, Lee J-G (2022) Learning from noisy labels with deep neural networks: a survey. IEEE Trans Neural Netw Learn Syst 34(11):8135\u20138153","journal-title":"IEEE Trans Neural Netw Learn Syst"},{"issue":"16","key":"11246_CR255","doi-asserted-by":"crossref","first-page":"1307","DOI":"10.21037\/atm-21-3457","volume":"9","author":"G Su","year":"2021","unstructured":"Su G, Lin B, Yin J, Luo W, Xu R, Xu J, Dong K (2021) Detection of hypomimia in patients with Parkinson\u2019s disease via smile videos. Ann Trans Med 9(16):1307","journal-title":"Ann Trans Med"},{"key":"11246_CR256","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/s40537-021-00414-0","volume":"8","author":"V Sampath","year":"2021","unstructured":"Sampath V, Maurtua I, Aguilar Martin JJ, Gutierrez A (2021) A survey on generative adversarial networks for imbalance problems in computer vision tasks. J Big Data 8:1\u201359","journal-title":"J Big Data"},{"key":"11246_CR257","doi-asserted-by":"crossref","DOI":"10.1016\/j.compeleceng.2020.106921","volume":"89","author":"U Saeed","year":"2021","unstructured":"Saeed U, Masood K, Dawood H (2021) Illumination normalization techniques for makeup-invariant face recognition. Comput Electrical Eng 89:106921","journal-title":"Comput Electrical Eng"},{"issue":"13","key":"11246_CR258","doi-asserted-by":"crossref","DOI":"10.1016\/j.heliyon.2024.e33858","volume":"10","author":"D Saksenberg","year":"2024","unstructured":"Saksenberg D, Mukherjee S, Zafar MA, Ziganshin B, Elefteriades JA (2024) Pilot study exploring artificial intelligence for facial-image-based diagnosis of Marfan syndrome. Heliyon 10(13):e33858","journal-title":"Heliyon"},{"issue":"4","key":"11246_CR259","doi-asserted-by":"crossref","first-page":"313","DOI":"10.1111\/joor.12486","volume":"44","author":"M Schimmel","year":"2017","unstructured":"Schimmel M, Ono T, Lam O, M\u00fcller F (2017) Oro-facial impairment in stroke patients. J Oral Rehabil 44(4):313\u2013326","journal-title":"J Oral Rehabil"},{"key":"11246_CR260","doi-asserted-by":"crossref","unstructured":"Stratou G, Scherer S, Gratch J, Morency L-P (2013) Automatic nonverbal behavior indicators of depression and ptsd: Exploring gender differences. In: 2013 Humaine Association Conference on Affective Computing and Intelligent Interaction, pp. 147\u2013152. IEEE","DOI":"10.1109\/ACII.2013.31"},{"key":"11246_CR261","doi-asserted-by":"crossref","unstructured":"Scherer S, Stratou G, Morency L-P (2013) Audiovisual behavior descriptors for depression assessment. In: Proceedings of the 15th ACM on International Conference on Multimodal Interaction, pp. 135\u2013140","DOI":"10.1145\/2522848.2522886"},{"key":"11246_CR262","doi-asserted-by":"publisher","DOI":"10.48550\/arXiv.1703.05175","author":"J Snell","year":"2017","unstructured":"Snell J, Swersky K, Zemel R (2017) Prototypical networks for few-shot learning. Adv Neural Inform Proc Syst. https:\/\/doi.org\/10.48550\/arXiv.1703.05175","journal-title":"Adv Neural Inform Proc Syst"},{"issue":"8","key":"11246_CR263","doi-asserted-by":"crossref","first-page":"827","DOI":"10.3390\/bioengineering11080827","volume":"11","author":"FF Sherif","year":"2024","unstructured":"Sherif FF, Tawfik N, Mousa D, Abdallah MS, Cho Y-I (2024) Automated multi-class facial syndrome classification using transfer learning techniques. Bioengineering 11(8):827","journal-title":"Bioengineering"},{"issue":"3","key":"11246_CR264","first-page":"371","volume":"9","author":"G Shafer","year":"2008","unstructured":"Shafer G, Vovk V (2008) A tutorial on conformal prediction. J Mach Learn Res 9(3):371\u2013421","journal-title":"J Mach Learn Res"},{"key":"11246_CR265","unstructured":"S\u00fcmer \u00d6, Waikel RL, Hanchard SEL, Duong D, Krawitz P, Conati C, Solomon BD, Andr\u00e9 E (2023) Region-based saliency explanations on the recognition of facial genetic syndromes. In: Machine Learning for Healthcare Conference, pp. 712\u2013736. PMLR"},{"key":"11246_CR266","unstructured":"Simonyan K, Zisserman A (2014) Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556"},{"issue":"6","key":"11246_CR267","doi-asserted-by":"crossref","first-page":"1756","DOI":"10.3390\/biomedicines11061756","volume":"11","author":"J Tang","year":"2023","unstructured":"Tang J, Han J, Xue J, Zhen L, Yang X, Pan M, Hu L, Li R, Jiang Y, Zhang Y et al (2023) A deep-learning-based method can detect both common and rare genetic disorders in fetal ultrasound. Biomedicines 11(6):1756","journal-title":"Biomedicines"},{"key":"11246_CR268","doi-asserted-by":"crossref","unstructured":"Thamilselvan R, Kalpana T, Natesan P, Showket S, et al (2024) Autism spectrum disorder diagnosis using deep learning techniques. In: 2024 International Conference on Cognitive Robotics and Intelligent Systems (ICC-ROBINS), pp. 402\u2013407. IEEE","DOI":"10.1109\/ICC-ROBINS60238.2024.10533978"},{"issue":"5","key":"11246_CR269","doi-asserted-by":"crossref","first-page":"1497","DOI":"10.1109\/JBHI.2017.2754861","volume":"22","author":"J Thevenot","year":"2017","unstructured":"Thevenot J, L\u00f3pez MB, Hadid A (2017) A survey on computer vision for assistive medical diagnosis from faces. IEEE J Biomed Health Inform 22(5):1497\u20131511","journal-title":"IEEE J Biomed Health Inform"},{"issue":"11","key":"11246_CR270","doi-asserted-by":"crossref","first-page":"30632","DOI":"10.2196\/30632","volume":"23","author":"C Tsou","year":"2021","unstructured":"Tsou C, Robinson S, Boyd J, Jamieson A, Blakeman R, Yeung J, McDonnell J, Waters S, Bosich K, Hendrie D (2021) Effectiveness of telehealth in rural and remote emergency departments: systematic review. J Med Internet Res 23(11):30632","journal-title":"J Med Internet Res"},{"key":"11246_CR271","doi-asserted-by":"crossref","first-page":"871","DOI":"10.1007\/s13246-020-00886-z","volume":"43","author":"U Thirunavukkarasu","year":"2020","unstructured":"Thirunavukkarasu U, Umapathy S, Janardhanan K, Thirunavukkarasu R (2020) A computer aided diagnostic method for the evaluation of type ii diabetes mellitus in facial thermograms. Phys Eng Sci Med 43:871\u2013888","journal-title":"Phys Eng Sci Med"},{"issue":"1","key":"11246_CR272","doi-asserted-by":"crossref","first-page":"1000","DOI":"10.1186\/s12909-024-05913-1","volume":"24","author":"S Baalen","year":"2024","unstructured":"Baalen S, Boon M (2024) Understanding disciplinary perspectives: a framework to develop skills for interdisciplinary research collaborations of medical experts and engineers. BMC Med Education 24(1):1000","journal-title":"BMC Med Education"},{"issue":"11","key":"11246_CR273","doi-asserted-by":"crossref","first-page":"1587","DOI":"10.1002\/mds.25658","volume":"28","author":"NM Kolk","year":"2013","unstructured":"Kolk NM, King LA (2013) Effects of exercise on mobility in people with Parkinson\u2019s disease. Mov Disorders 28(11):1587\u20131596","journal-title":"Mov Disorders"},{"issue":"11","key":"11246_CR274","first-page":"2579","volume":"9","author":"L Maaten","year":"2008","unstructured":"Maaten L, Hinton G (2008) Visualizing data using t-sne. J Mach Learn Res 9(11):2579\u20132605","journal-title":"J Mach Learn Res"},{"key":"11246_CR275","doi-asserted-by":"crossref","unstructured":"Viola P, Jones M (2001) Rapid object detection using a boosted cascade of simple features. In: Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, vol. 1, Ieee","DOI":"10.1109\/CVPR.2001.990517"},{"key":"11246_CR276","doi-asserted-by":"crossref","unstructured":"Valstar M, Schuller B, Smith K, Eyben F, Jiang B, Bilakhia S, Schnieder S, Cowie R, Pantic M (2013) Avec 2013: the continuous audio\/visual emotion and depression recognition challenge. In: Proceedings of the 3rd ACM International Workshop on Audio\/visual Emotion Challenge, pp. 3\u201310","DOI":"10.1145\/2512530.2512533"},{"key":"11246_CR277","doi-asserted-by":"crossref","unstructured":"Valstar M, Schuller B, Smith K, Almaev T, Eyben F, Krajewski J, Cowie R, Pantic M (2014) Avec 2014: 3d dimensional affect and depression recognition challenge. In: Proceedings of the 4th International Workshop on Audio\/visual Emotion Challenge, pp. 3\u201310","DOI":"10.1145\/2661806.2661807"},{"key":"11246_CR278","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-57959-7","volume-title":"The eu general data protection regulation (gdpr). A practical guide","author":"P Voigt","year":"2017","unstructured":"Voigt P, Bussche A (2017) The eu general data protection regulation (gdpr). A practical guide. Springer International Publishing, Cham"},{"issue":"8","key":"11246_CR279","doi-asserted-by":"crossref","first-page":"509","DOI":"10.1136\/jmg.24.8.509","volume":"24","author":"RM Winter","year":"1987","unstructured":"Winter RM, Baraitser M (1987) The London dysmorphology database. J Med Gene 24(8):509","journal-title":"J Med Gene"},{"key":"11246_CR280","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W (2020) Mitigating bias in face recognition using skewness-aware reinforcement learning. In: Proceedings of the IEEE\/CVF Conference on Computer Vision and Pattern Recognition, pp. 9322\u20139331","DOI":"10.1109\/CVPR42600.2020.00934"},{"key":"11246_CR281","doi-asserted-by":"crossref","unstructured":"Wang M, Deng W, Hu J, Tao X, Huang Y (2019) Racial faces in the wild: Reducing racial bias by information maximization adaptation network. In: Proceedings of the Ieee\/cvf International Conference on Computer Vision, pp. 692\u2013702","DOI":"10.1109\/ICCV.2019.00078"},{"key":"11246_CR282","unstructured":"Wei\u00df RH (2006) CFT 20-R: Grundintelligenztest Skala 2-revision. Hogrefe"},{"issue":"5","key":"11246_CR283","doi-asserted-by":"crossref","first-page":"328","DOI":"10.1159\/000502211","volume":"110","author":"R Wei","year":"2020","unstructured":"Wei R, Jiang C, Gao J, Xu P, Zhang D, Sun Z, Liu X, Deng K, Bao X, Sun G et al (2020) Deep-learning approach to automatic identification of facial anomalies in endocrine disorders. Neuroendocrinology 110(5):328\u2013337","journal-title":"Neuroendocrinology"},{"issue":"7","key":"11246_CR284","doi-asserted-by":"crossref","first-page":"1432","DOI":"10.1109\/TIFS.2015.2414392","volume":"10","author":"L Wen","year":"2015","unstructured":"Wen L, Li X, Guo G, Zhu Y (2015) Automated depression diagnosis based on facial dynamic analysis and sparse coding. IEEE Trans Inform Forensics Secur 10(7):1432\u20131441","journal-title":"IEEE Trans Inform Forensics Secur"},{"key":"11246_CR285","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1186\/1750-1172-4-3","volume":"4","author":"LC Wijesekera","year":"2009","unstructured":"Wijesekera LC, Nigel Leigh P (2009) Amyotrophic lateral sclerosis. Orphanet J Rare Dis 4:1\u201322","journal-title":"Orphanet J Rare Dis"},{"issue":"4","key":"11246_CR286","volume":"18","author":"D Wu","year":"2024","unstructured":"Wu D, Qiang J, Hong W, Du H, Yang H, Zhu H, Pan H, Shen Z, Chen S (2024) Artificial intelligence facial recognition system for diagnosis of endocrine and metabolic syndromes based on a facial image database. Diabetes Metabolic Syndrome: Clin Res Rev 18(4):103003","journal-title":"Diabetes Metabolic Syndrome: Clin Res Rev"},{"issue":"1","key":"11246_CR287","doi-asserted-by":"crossref","first-page":"2418984","DOI":"10.1080\/19490976.2024.2418984","volume":"16","author":"Y Wan","year":"2024","unstructured":"Wan Y, Wong OW, Tun HM, Su Q, Xu Z, Tang W, Ma SL, Chan S, Chan FK, Ng SC (2024) Fecal microbial marker panel for aiding diagnosis of autism spectrum disorders. Gut Microbes 16(1):2418984","journal-title":"Gut Microbes"},{"key":"11246_CR288","unstructured":"Wu D, Yang J, Liu C, Hsieh T-C, Marchi E, Blair J, Krawitz P, Weng C, Chung W, Lyon GJ, et al (2024) Gestaltmml: Enhancing rare genetic disease diagnosis through multimodal machine learning combining facial images and clinical texts. ArXiv"},{"key":"11246_CR289","doi-asserted-by":"crossref","unstructured":"Wang Y, Ye Y, Shi S, Mao K, Zheng H, Chen X, Yan H, Lu Y, Zhou Y, Ye W, et al (2024) Prediagnosis recognition of acute ischemic stroke by artificial intelligence from facial images. Aging Cell, 14196","DOI":"10.1111\/acel.14196"},{"issue":"9","key":"11246_CR290","doi-asserted-by":"crossref","first-page":"2535","DOI":"10.1210\/clinem\/dgab371","volume":"106","author":"H Wang","year":"2021","unstructured":"Wang H, Zhang W, Li S, Fan Y, Feng M, Wang R (2021) Development and evaluation of deep learning-based automated segmentation of pituitary adenoma in clinical task. J Clin Endocrinol Metabolism 106(9):2535\u20132546","journal-title":"J Clin Endocrinol Metabolism"},{"issue":"3","key":"11246_CR291","first-page":"1","volume":"58","author":"Y Xu","year":"2025","unstructured":"Xu Y, Khan TM, Song Y, Meijering E (2025) Edge deep learning in computer vision and medical diagnostics: a comprehensive survey. Artif Intel Rev 58(3):1\u201378","journal-title":"Artif Intel Rev"},{"issue":"1","key":"11246_CR292","doi-asserted-by":"crossref","first-page":"92","DOI":"10.1038\/s41537-022-00287-z","volume":"8","author":"S Xu","year":"2022","unstructured":"Xu S, Yang Z, Chakraborty D, Chua YHV, Tolomeo S, Winkler S, Birnbaum M, Tan B-L, Lee J, Dauwels J (2022) Identifying psychiatric manifestations in schizophrenia and depression from audio-visual behavioural indicators through a machine-learning approach. Schizophrenia 8(1):92","journal-title":"Schizophrenia"},{"issue":"5","key":"11246_CR293","doi-asserted-by":"crossref","first-page":"429","DOI":"10.1192\/bjp.133.5.429","volume":"133","author":"RC Young","year":"1978","unstructured":"Young RC, Biggs JT, Ziegler VE, Meyer DA (1978) A rating scale for mania: reliability, validity and sensitivity. British J Psychiatry 133(5):429\u2013435","journal-title":"British J Psychiatry"},{"key":"11246_CR294","doi-asserted-by":"crossref","unstructured":"Yun S, Han D, Oh SJ, Chun S, Choe J, Yoo Y (2019) Cutmix: Regularization strategy to train strong classifiers with localizable features. In: Proceedings of the IEEE\/CVF International Conference on Computer Vision, pp. 6023\u20136032","DOI":"10.1109\/ICCV.2019.00612"},{"key":"11246_CR295","volume":"12","author":"H Yang","year":"2021","unstructured":"Yang H, Hu X-R, Sun L, Hong D, Zheng Y-Y, Xin Y, Liu H, Lin M-Y, Wen L, Liang D-P et al (2021) Automated facial recognition for Noonan syndrome using novel deep convolutional neural network with additive angular margin loss. Front Gene 12:669841","journal-title":"Front Gene"},{"issue":"1","key":"11246_CR296","doi-asserted-by":"crossref","first-page":"186","DOI":"10.1186\/s13195-022-01131-3","volume":"14","author":"Q Yang","year":"2022","unstructured":"Yang Q, Li X, Ding X, Xu F, Ling Z (2022) Deep learning-based speech analysis for Alzheimer\u2019s disease detection: a literature review. Alzheimer\u2019s Res Therapy 14(1):186","journal-title":"Alzheimer\u2019s Res Therapy"},{"key":"11246_CR297","unstructured":"Yi D, Lei Z, Liao S, Li SZ (2014) Learning face representation from scratch. arXiv preprint arXiv:1411.7923"},{"issue":"9","key":"11246_CR298","doi-asserted-by":"crossref","first-page":"1883","DOI":"10.1038\/s41591-022-01966-1","volume":"28","author":"Y Yang","year":"2022","unstructured":"Yang Y, Lyu J, Wang R, Wen Q, Zhao L, Chen W, Bi S, Meng J, Mao K, Xiao Y et al (2022) A digital mask to safeguard patient privacy. Nat Med 28(9):1883\u20131892","journal-title":"Nat Med"},{"issue":"1","key":"11246_CR299","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1038\/s41746-023-00805-y","volume":"6","author":"J Yang","year":"2023","unstructured":"Yang J, Soltan AA, Eyre DW, Yang Y, Clifton DA (2023) An adversarial training framework for mitigating algorithmic biases in clinical machine learning. NPJ Digital Med 6(1):55","journal-title":"NPJ Digital Med"},{"issue":"8","key":"11246_CR300","doi-asserted-by":"crossref","first-page":"3698","DOI":"10.1109\/JBHI.2023.3260816","volume":"27","author":"J Ye","year":"2023","unstructured":"Ye J, Yu Y, Fu G, Zheng Y, Liu Y, Zhu Y, Wang Q (2023) Analysis and recognition of voluntary facial expression mimicry based on depressed patients. IEEE J Biomed Health Inform 27(8):3698\u20133709","journal-title":"IEEE J Biomed Health Inform"},{"key":"11246_CR301","unstructured":"Zhang Y, Gu S, Song J, Pan B, Bai G, Zhao L (2023) Xai benchmark for visual explanation. arXiv preprint arXiv:2310.08537"},{"key":"11246_CR302","unstructured":"Zhang H (2017) mixup: Beyond empirical risk minimization. arXiv preprint arXiv:1710.09412"},{"issue":"9","key":"11246_CR303","doi-asserted-by":"crossref","first-page":"9439","DOI":"10.1109\/TCYB.2021.3056104","volume":"52","author":"A Zhao","year":"2021","unstructured":"Zhao A, Li J, Dong J, Qi L, Zhang Q, Li N, Wang X, Zhou H (2021) Multimodal gait recognition for neurodegenerative diseases. IEEE Trans Cybern 52(9):9439\u20139453","journal-title":"IEEE Trans Cybern"},{"key":"11246_CR304","doi-asserted-by":"crossref","unstructured":"Zhang BH, Lemoine B, Mitchell M (2018) Mitigating unwanted biases with adversarial learning. In: Proceedings of the 2018 AAAI\/ACM Conference on AI, Ethics, and Society, pp. 335\u2013340 (2018)","DOI":"10.1145\/3278721.3278779"},{"key":"11246_CR305","first-page":"55140","volume":"36","author":"A Zhou","year":"2023","unstructured":"Zhou A, Li S, Sriram P, Li X, Dong J, Sharma A, Zhong Y, Luo S, Kindratenko V, Heintz G et al (2023) Youtubepd: a multimodal benchmark for Parkinson\u2019s disease analysis. Adv Neural Inform Proc Syst 36:55140\u201355159","journal-title":"Adv Neural Inform Proc Syst"},{"issue":"5","key":"11246_CR306","doi-asserted-by":"crossref","first-page":"3002","DOI":"10.1002\/brb3.3002","volume":"13","author":"X Zhang","year":"2023","unstructured":"Zhang X, Li T, Wang C, Tian T, Pang H, Pang J, Su C, Shi X, Li J, Ren L et al (2023) Recognizing schizophrenia using facial expressions based on convolutional neural network. Brain Behav 13(5):3002","journal-title":"Brain Behav"},{"key":"11246_CR307","doi-asserted-by":"crossref","unstructured":"Zhu J-Y, Park T, Isola P, Efros AA (2017) Unpaired image-to-image translation using cycle-consistent adversarial networks. In: Proceedings of the IEEE International Conference on Computer Vision, pp. 2223\u20132232","DOI":"10.1109\/ICCV.2017.244"},{"issue":"4","key":"11246_CR308","doi-asserted-by":"crossref","first-page":"578","DOI":"10.1109\/TAFFC.2017.2650899","volume":"9","author":"Y Zhu","year":"2017","unstructured":"Zhu Y, Shang Y, Shao Z, Guo G (2017) Automated depression diagnosis based on deep networks to encode facial appearance and dynamics. IEEE Trans Affective Comput 9(4):578\u2013584","journal-title":"IEEE Trans Affective Comput"},{"issue":"1","key":"11246_CR309","doi-asserted-by":"crossref","first-page":"63","DOI":"10.1001\/archpsyc.1965.01720310065008","volume":"12","author":"WW Zung","year":"1965","unstructured":"Zung WW (1965) A self-rating depression scale. Archives General Psychiatry 12(1):63\u201370","journal-title":"Archives General Psychiatry"},{"key":"11246_CR310","doi-asserted-by":"crossref","unstructured":"Zung WW (1971) Self-rating anxiety scale. BMC Psychiatry","DOI":"10.1037\/t04092-000"},{"key":"11246_CR311","doi-asserted-by":"crossref","unstructured":"Zeevi T, Venkataraman R, Staib LH, Onofrey JA (2024) Monte-carlo frequency dropout for predictive uncertainty estimation in deep learning. In: 2024 IEEE International Symposium on Biomedical Imaging (ISBI), pp. 1\u20135. IEEE","DOI":"10.1109\/ISBI56570.2024.10635511"},{"key":"11246_CR312","doi-asserted-by":"crossref","first-page":"90","DOI":"10.1016\/j.jad.2016.12.022","volume":"210","author":"JC Zwick","year":"2017","unstructured":"Zwick JC, Wolkenstein L (2017) Facial emotion recognition, theory of mind and the role of facial mimicry in depression. J Affective Disord 210:90\u201399","journal-title":"J Affective Disord"},{"key":"11246_CR313","doi-asserted-by":"crossref","unstructured":"Zeng K, Wang Z, Lu T, Chen J, Liang C, Han Z (2025) Multi-stage statistical texture-guided gan for tilted face frontalization. IEEE Transactions on Image Processing","DOI":"10.1109\/TIP.2025.3548896"},{"issue":"10","key":"11246_CR314","doi-asserted-by":"crossref","first-page":"1499","DOI":"10.1109\/LSP.2016.2603342","volume":"23","author":"K Zhang","year":"2016","unstructured":"Zhang K, Zhang Z, Li Z, Qiao Y (2016) Joint face detection and alignment using multitask cascaded convolutional networks. IEEE Signal Proc Lett 23(10):1499\u20131503","journal-title":"IEEE Signal Proc Lett"},{"issue":"5","key":"11246_CR315","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1145\/3708501","volume":"57","author":"R Zhao","year":"2023","unstructured":"Zhao R, Zhang Y, Wang T, Wen W, Xiang Y, Cao X (2023) Visual content privacy protection: a survey. ACM Comput Surv. 57(5):1\u201336","journal-title":"ACM Comput Surv."}],"container-title":["Artificial Intelligence Review"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11246-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s10462-025-11246-2\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s10462-025-11246-2.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,6,23]],"date-time":"2025-06-23T10:36:35Z","timestamp":1750674995000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s10462-025-11246-2"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,5,13]]},"references-count":314,"journal-issue":{"issue":"8","published-online":{"date-parts":[[2025,8]]}},"alternative-id":["11246"],"URL":"https:\/\/doi.org\/10.1007\/s10462-025-11246-2","relation":{},"ISSN":["1573-7462"],"issn-type":[{"value":"1573-7462","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,5,13]]},"assertion":[{"value":"25 April 2025","order":1,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"13 May 2025","order":2,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"The authors declare that they have no competing interests.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}},{"value":"This paper does not contain any studies with human participants or animals performed by any of the authors.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethical approval"}},{"value":"Not applicable.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"Not applicable.","order":5,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent for publication"}}],"article-number":"243"}}