{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,1]],"date-time":"2026-05-01T02:17:18Z","timestamp":1777601838485,"version":"3.51.4"},"reference-count":45,"publisher":"Springer Science and Business Media LLC","issue":"1","license":[{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"},{"start":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T00:00:00Z","timestamp":1740268800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0"}],"funder":[{"DOI":"10.13039\/501100000265","name":"RCUK | Medical Research Council","doi-asserted-by":"publisher","award":["MRC\/CIC8\/81"],"award-info":[{"award-number":["MRC\/CIC8\/81"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"RCUK | Medical Research Council","doi-asserted-by":"publisher","award":["MRC\/CIC8\/81"],"award-info":[{"award-number":["MRC\/CIC8\/81"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100000265","name":"RCUK | Medical Research Council","doi-asserted-by":"publisher","award":["MRC\/CIC8\/81"],"award-info":[{"award-number":["MRC\/CIC8\/81"]}],"id":[{"id":"10.13039\/501100000265","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["npj Digit. Med."],"abstract":"<jats:title>Abstract<\/jats:title>\n          <jats:p>Remote monitoring is essential for healthcare digital transformation, however, this poses greater burdens on healthcare providers to review and respond as the data collected expands. This study developed a multimodal neural network to automate assessments of patient-generated data from remote postoperative wound monitoring. Two interventional studies including adult gastrointestinal surgery patients collected wound images and patient-reported outcome measures (PROMs) for 30-days postoperatively. Neural networks for PROMs and images were combined to predict surgical site infection (SSI) diagnosis within 48\u2009h. The multimodal neural network model to predict confirmed SSI within 48\u2009h remained comparable to clinician triage (0.762 [0.690\u20130.835] vs 0.777 [0.721\u20130.832]), with an excellent performance on external validation. Simulated usage indicated an 80% reduction in staff time (51.5 to 9.1\u2009h) without compromising diagnostic accuracy. This multimodal approach can effectively support remote monitoring, alleviating provider burden while ensuring high-quality postoperative care.<\/jats:p>","DOI":"10.1038\/s41746-024-01419-8","type":"journal-article","created":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T13:29:21Z","timestamp":1740317361000},"update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":15,"title":["Multimodal machine learning to predict surgical site infection with healthcare workload impact assessment"],"prefix":"10.1038","volume":"8","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-6482-9086","authenticated-orcid":false,"given":"Kenneth A.","family":"McLean","sequence":"first","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5151-8342","authenticated-orcid":false,"given":"Alessandro","family":"Sgr\u00f2","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6181-7020","authenticated-orcid":false,"given":"Leo R.","family":"Brown","sequence":"additional","affiliation":[]},{"given":"Louis F.","family":"Buijs","sequence":"additional","affiliation":[]},{"given":"Katie E.","family":"Mountain","sequence":"additional","affiliation":[]},{"given":"Catherine A.","family":"Shaw","sequence":"additional","affiliation":[]},{"given":"Thomas M.","family":"Drake","sequence":"additional","affiliation":[]},{"given":"Riinu","family":"Pius","sequence":"additional","affiliation":[]},{"given":"Stephen R.","family":"Knight","sequence":"additional","affiliation":[]},{"given":"Cameron J.","family":"Fairfield","sequence":"additional","affiliation":[]},{"given":"Richard J. E.","family":"Skipworth","sequence":"additional","affiliation":[]},{"given":"Sotirios A.","family":"Tsaftaris","sequence":"additional","affiliation":[]},{"given":"Stephen J.","family":"Wigmore","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1417-7515","authenticated-orcid":false,"given":"Mark A.","family":"Potter","sequence":"additional","affiliation":[]},{"given":"Matt-Mouley","family":"Bouamrane","sequence":"additional","affiliation":[]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5018-3066","authenticated-orcid":false,"given":"Ewen M.","family":"Harrison","sequence":"additional","affiliation":[]},{"name":"TWIST Collaborators","sequence":"additional","affiliation":[]},{"given":"K.","family":"Baweja","sequence":"additional","affiliation":[]},{"given":"W. A.","family":"Cambridge","sequence":"additional","affiliation":[]},{"given":"V.","family":"Chauhan","sequence":"additional","affiliation":[]},{"given":"K.","family":"Czyzykowska","sequence":"additional","affiliation":[]},{"given":"M.","family":"Edirisooriya","sequence":"additional","affiliation":[]},{"given":"A.","family":"Forsyth","sequence":"additional","affiliation":[]},{"given":"B.","family":"Fox","sequence":"additional","affiliation":[]},{"given":"J.","family":"Fretwell","sequence":"additional","affiliation":[]},{"given":"C.","family":"Gent","sequence":"additional","affiliation":[]},{"given":"A.","family":"Gherman","sequence":"additional","affiliation":[]},{"given":"L.","family":"Green","sequence":"additional","affiliation":[]},{"given":"J.","family":"Grewar","sequence":"additional","affiliation":[]},{"given":"S.","family":"Heelan","sequence":"additional","affiliation":[]},{"given":"D.","family":"Henshall","sequence":"additional","affiliation":[]},{"given":"C.","family":"Iiuoma","sequence":"additional","affiliation":[]},{"given":"S.","family":"Jayasangaran","sequence":"additional","affiliation":[]},{"given":"C.","family":"Johnston","sequence":"additional","affiliation":[]},{"given":"E.","family":"Kennedy","sequence":"additional","affiliation":[]},{"given":"D.","family":"Kremel","sequence":"additional","affiliation":[]},{"given":"J.","family":"Kung","sequence":"additional","affiliation":[]},{"given":"J.","family":"Kwong","sequence":"additional","affiliation":[]},{"given":"C.","family":"Leavy","sequence":"additional","affiliation":[]},{"given":"J.","family":"Liu","sequence":"additional","affiliation":[]},{"given":"S.","family":"Mackay","sequence":"additional","affiliation":[]},{"given":"A.","family":"MacNamara","sequence":"additional","affiliation":[]},{"given":"S.","family":"Mowitt","sequence":"additional","affiliation":[]},{"given":"E.","family":"Musenga","sequence":"additional","affiliation":[]},{"given":"N.","family":"Ng","sequence":"additional","affiliation":[]},{"given":"Z. H.","family":"Ng","sequence":"additional","affiliation":[]},{"given":"S.","family":"O\u2019Neill","sequence":"additional","affiliation":[]},{"given":"M.","family":"Ramage","sequence":"additional","affiliation":[]},{"given":"J.","family":"Reed","sequence":"additional","affiliation":[]},{"given":"A.","family":"Riad","sequence":"additional","affiliation":[]},{"given":"C.","family":"Scott","sequence":"additional","affiliation":[]},{"given":"V.","family":"Sehgal","sequence":"additional","affiliation":[]},{"given":"A.","family":"Sgr\u00f2","sequence":"additional","affiliation":[]},{"given":"L.","family":"Steven","sequence":"additional","affiliation":[]},{"given":"B.","family":"Stutchfield","sequence":"additional","affiliation":[]},{"given":"S.","family":"Tominey","sequence":"additional","affiliation":[]},{"given":"W.","family":"Wilson","sequence":"additional","affiliation":[]},{"given":"M.","family":"Wojtowicz","sequence":"additional","affiliation":[]},{"given":"J.","family":"Yang","sequence":"additional","affiliation":[]}],"member":"297","published-online":{"date-parts":[[2025,2,23]]},"reference":[{"key":"1419_CR1","unstructured":"Royal College of Surgeons of England (RCSEng). Future of Surgery (RCSEng, 2018)."},{"key":"1419_CR2","unstructured":"World Health Organization (WHO). Global strategy on digital health 2020-2025 (World Health Organization, 2021)."},{"key":"1419_CR3","unstructured":"House of Commons Public Accounts Committee. Digital transformation in the NHS: Twenty-Second Report of Session 2019-20 report, together with formal minutes (House of Commons Public Accounts Committee, 2020)."},{"key":"1419_CR4","unstructured":"Scottish Government. Digital Health and Care Strategy: Report of the External Expert Panel (Scottish Government, 2018)."},{"key":"1419_CR5","doi-asserted-by":"publisher","first-page":"843","DOI":"10.1016\/j.amepre.2016.06.008","volume":"51","author":"E Murray","year":"2016","unstructured":"Murray, E. et al. Evaluating Digital Health Interventions: Key Questions and Approaches. Am. J. Prev. Med. 51, 843\u2013851 (2016).","journal-title":"Am. J. Prev. Med."},{"key":"1419_CR6","unstructured":"NHS England Transformation Directorate. Digital playbooks, http:\/\/transform.england.nhs.uk\/key-tools-and-info\/digital-playbooks\/ (2023)."},{"key":"1419_CR7","doi-asserted-by":"publisher","first-page":"e295","DOI":"10.1016\/S2589-7500(23)00026-2","volume":"5","author":"KA McLean","year":"2023","unstructured":"McLean, K. A. et al. Readiness for implementation of novel digital health interventions for postoperative monitoring: a systematic review and clinical innovation network analysis (CINA) according to the IDEAL Framework. Lancet Digit. Health 5, e295\u2013e315 (2023).","journal-title":"Lancet Digit. Health"},{"key":"1419_CR8","doi-asserted-by":"publisher","first-page":"e140","DOI":"10.1097\/PTS.0000000000000720","volume":"18","author":"JR Burke","year":"2022","unstructured":"Burke, J. R., Downey, C. & Almoudaris, A. M. Failure to Rescue Deteriorating Patients: A Systematic Review of Root Causes and Improvement Strategies. J. Patient Saf. 18, e140\u2013e155 (2022).","journal-title":"J. Patient Saf."},{"key":"1419_CR9","doi-asserted-by":"publisher","first-page":"130","DOI":"10.1510\/icvts.2010.249474","volume":"12","author":"CA Efthymiou","year":"2011","unstructured":"Efthymiou, C. A. & O\u2019Regan, D. J. Postdischarge complications: what exactly happens when the patient goes home? Interact. Cardiovasc. Thorac. Surg. 12, 130\u2013134 (2011).","journal-title":"Interact. Cardiovasc. Thorac. Surg."},{"key":"1419_CR10","unstructured":"World Health Organization (WHO). Telemedicine: opportunities and developments in Member States: report on the second global survey on eHealth (World Health Organization, 2010)."},{"key":"1419_CR11","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/s12911-024-02670-5","volume":"24","author":"KA McLean","year":"2024","unstructured":"McLean, K. A. et al. Implementation of digital remote postoperative monitoring in routine practice: a qualitative study of barriers and facilitators. BMC Med. Inform. Decis. Mak. 24, 307 (2024).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"1419_CR12","doi-asserted-by":"publisher","first-page":"115","DOI":"10.1038\/nature21056","volume":"542","author":"A Esteva","year":"2017","unstructured":"Esteva, A. et al. Dermatologist-level classification of skin cancer with deep neural networks. Nature 542, 115\u2013118 (2017).","journal-title":"Nature"},{"key":"1419_CR13","unstructured":"Goodfellow, I., Bengio, Y., & Courville, A. Deep Learning (MIT Press, 2016)."},{"key":"1419_CR14","unstructured":"National Institute for Health and Clinical Excellence (NICE). Quality standard [QS113]: Healthcare-associated infections (National Institute for Health and Clinical Excellence (NICE), 2016)."},{"key":"1419_CR15","doi-asserted-by":"publisher","first-page":"516","DOI":"10.1016\/S1473-3099(18)30101-4","volume":"18","author":"GlobalSurg Collaborative.","year":"2018","unstructured":"GlobalSurg Collaborative. Surgical site infection after gastrointestinal surgery in high-income, middle-income, and low-income countries: a prospective, international, multicentre cohort study. Lancet Infect. Dis. 18, 516\u2013525 (2018).","journal-title":"Lancet Infect. Dis."},{"key":"1419_CR16","doi-asserted-by":"publisher","first-page":"267","DOI":"10.1053\/jhin.2001.1003","volume":"48","author":"P Astagneau","year":"2001","unstructured":"Astagneau, P., Rioux, C., Golliot, F., Br\u00fccker, G. & INCISO Network Study Group. Morbidity and mortality associated with surgical site infections: results from the 1997\u20131999 INCISO surveillance. J. Hosp. Infect. 48, 267\u2013274 (2001).","journal-title":"J. Hosp. Infect."},{"key":"1419_CR17","doi-asserted-by":"publisher","first-page":"1126","DOI":"10.1016\/j.jval.2015.08.004","volume":"18","author":"A Gheorghe","year":"2015","unstructured":"Gheorghe, A. et al. Health Utility Values Associated with Surgical Site Infection: A Systematic Review. Value Health J. Int. Soc. Pharmacoeconomics Outcomes Res. 18, 1126\u20131137 (2015).","journal-title":"Value Health J. Int. Soc. Pharmacoeconomics Outcomes Res."},{"key":"1419_CR18","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00526-0","volume":"4","author":"KA McLean","year":"2021","unstructured":"McLean, K. A. et al. Remote diagnosis of surgical-site infection using a mobile digital intervention: a randomised controlled trial in emergency surgery patients. npj Digital Med. 4, 160 (2021).","journal-title":"npj Digital Med."},{"key":"1419_CR19","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-023-00824-9","volume":"6","author":"KA McLean","year":"2023","unstructured":"McLean, K. A. et al. Evaluation of remote digital postoperative wound monitoring in routine surgical practice. npj Digital Med. 6, 85 (2023).","journal-title":"npj Digital Med."},{"key":"1419_CR20","unstructured":"Centre for Disease Control and Prevention (CDC) National Healthcare Safety Network (NHSN). Patient Safety Component (PSC) Manual Chapter 9: Surgical site infection (SSI) event (CDC, Atlanta, 2016)."},{"key":"1419_CR21","doi-asserted-by":"publisher","first-page":"307","DOI":"10.1186\/s12911-024-02670-5","volume":"24","author":"K McLean","year":"2024","unstructured":"McLean, K. et al. Barriers and facilitators to implementation of digital remote postoperative monitoring in routine practice: a qualitative study. BMC Med. Inform. Decis. Mak. 24, 307 (2024).","journal-title":"BMC Med. Inform. Decis. Mak."},{"key":"1419_CR22","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-022-00655-0","volume":"5","author":"R Lathan","year":"2022","unstructured":"Lathan, R. et al. Diagnostic accuracy of telemedicine for detection of surgical site infection: a systematic review and meta-analysis. npj Digital Med. 5, 108 (2022).","journal-title":"npj Digital Med."},{"key":"1419_CR23","doi-asserted-by":"publisher","first-page":"020341","DOI":"10.7189\/jogh.10.020349","volume":"10","author":"CL Reddy","year":"2020","unstructured":"Reddy, C. L., Vervoort, D., Meara, J. G. & Atun, R. Surgery and universal health coverage: Designing an essential package for surgical care expansion and scale-up. J. Glob. Health 10, 020341 (2020).","journal-title":"J. Glob. Health"},{"key":"1419_CR24","doi-asserted-by":"crossref","first-page":"1440","DOI":"10.1002\/bjs.11646","volume":"107","author":"COVIDSurg Collaborative.","year":"2020","unstructured":"COVIDSurg Collaborative. Elective surgery cancellations due to the COVID-19 pandemic: global predictive modelling to inform surgical recovery plans. Br. J. Surg. 107, 1440\u20131449 (2020).","journal-title":"Br. J. Surg."},{"key":"1419_CR25","doi-asserted-by":"publisher","DOI":"10.1016\/j.artint.2022.103839","volume":"316","author":"O Wysocki","year":"2023","unstructured":"Wysocki, O. et al. Assessing the communication gap between AI models and healthcare professionals: Explainability, utility and trust in AI-driven clinical decision-making. Artif. Intell. 316, 103839 (2023).","journal-title":"Artif. Intell."},{"key":"1419_CR26","doi-asserted-by":"publisher","first-page":"890","DOI":"10.1093\/jamia\/ocaa268","volume":"28","author":"TP Quinn","year":"2021","unstructured":"Quinn, T. P., Senadeera, M., Jacobs, S., Coghlan, S. & Le, V. Trust and medical AI: the challenges we face and the expertise needed to overcome them. J. Am. Med Inf. Assoc. 28, 890\u2013894 (2021).","journal-title":"J. Am. Med Inf. Assoc."},{"key":"1419_CR27","doi-asserted-by":"publisher","DOI":"10.1038\/s41746-021-00509-1","volume":"4","author":"JP Richardson","year":"2021","unstructured":"Richardson, J. P. et al. Patient apprehensions about the use of artificial intelligence in healthcare. npj Digital Med. 4, 140 (2021).","journal-title":"npj Digital Med."},{"key":"1419_CR28","doi-asserted-by":"crossref","unstructured":"Shenoy, V. N., Foster, E., Aalami, L., Majeed, B. & Aalami, O. Deepwound: Automated Postoperative Wound Assessment and Surgical Site Surveillance through Convolutional Neural Networks. IEEE International Conference on Bioinformatics and Biomedicine (BIBM). 1017\u20131021 (IEEE, 2018).","DOI":"10.1109\/BIBM.2018.8621130"},{"key":"1419_CR29","doi-asserted-by":"publisher","DOI":"10.1177\/23821205211025855","volume":"8","author":"T Kaundinya","year":"2021","unstructured":"Kaundinya, T. & Kundu, R. V. Diversity of Skin Images in Medical Texts: Recommendations for Student Advocacy in Medical Education. J. Med Educ. Curric. Dev. 8, 23821205211025855 (2021).","journal-title":"J. Med Educ. Curric. Dev."},{"key":"1419_CR30","doi-asserted-by":"publisher","first-page":"157","DOI":"10.1016\/j.jaad.2021.06.884","volume":"87","author":"LN Guo","year":"2022","unstructured":"Guo, L. N., Lee, M. S., Kassamali, B., Mita, C. & Nambudiri, V. E. Bias in, bias out: Underreporting and underrepresentation of diverse skin types in machine learning research for skin cancer detection\u2014A scoping review. J. Am. Acad. Dermatol. 87, 157\u2013159 (2022).","journal-title":"J. Am. Acad. Dermatol."},{"key":"1419_CR31","doi-asserted-by":"publisher","first-page":"e2030932","DOI":"10.1001\/jamanetworkopen.2020.30932","volume":"3","author":"D Choi","year":"2020","unstructured":"Choi, D. et al. Development of a Machine Learning Model Using Multiple, Heterogeneous Data Sources to Estimate Weekly US Suicide Fatalities. JAMA Netw. Open 3, e2030932 (2020).","journal-title":"JAMA Netw. Open"},{"key":"1419_CR32","doi-asserted-by":"publisher","first-page":"e415","DOI":"10.1016\/S2589-7500(22)00049-8","volume":"4","author":"L Rasmy","year":"2022","unstructured":"Rasmy, L. et al. Recurrent neural network models (CovRNN) for predicting outcomes of patients with COVID-19 on admission to hospital: model development and validation using electronic health record data. Lancet Digital Health 4, e415\u2013e425 (2022).","journal-title":"Lancet Digital Health"},{"key":"1419_CR33","doi-asserted-by":"publisher","first-page":"361","DOI":"10.1093\/jamia\/ocw112","volume":"24","author":"E Choi","year":"2017","unstructured":"Choi, E., Schuetz, A., Stewart, W. F. & Sun, J. Using recurrent neural network models for early detection of heart failure onset. J. Am. Med Inf. Assoc. 24, 361\u2013370 (2017).","journal-title":"J. Am. Med Inf. Assoc."},{"key":"1419_CR34","doi-asserted-by":"publisher","first-page":"2200250","DOI":"10.1183\/13993003.00250-2022","volume":"60","author":"FS Van Royen","year":"2022","unstructured":"Van Royen, F. S., Moons, K. G. M., Geersing, G.-J. & van Smeden, M. Developing, validating, updating and judging the impact of prognostic models for respiratory diseases. Eur. Respir. J. 60, 2200250 (2022).","journal-title":"Eur. Respir. J."},{"key":"1419_CR35","doi-asserted-by":"publisher","first-page":"e100450","DOI":"10.1136\/bmjhci-2021-100450","volume":"28","author":"AS Ian","year":"2021","unstructured":"Ian, A. S., Stacy, M. C. & Enrico, C. Exploring stakeholder attitudes towards AI in clinical practice. BMJ Health Care Inform. 28, e100450 (2021).","journal-title":"BMJ Health Care Inform."},{"key":"1419_CR36","doi-asserted-by":"publisher","first-page":"e39742","DOI":"10.2196\/39742","volume":"25","author":"HDJ Hogg","year":"2023","unstructured":"Hogg, H. D. J. et al. Stakeholder Perspectives of Clinical Artificial Intelligence Implementation: Systematic Review of Qualitative Evidence. J. Med. Internet Res. 25, e39742 (2023).","journal-title":"J. Med. Internet Res."},{"key":"1419_CR37","doi-asserted-by":"publisher","DOI":"10.1126\/sciadv.abq6147","volume":"8","author":"R Daneshjou","year":"2022","unstructured":"Daneshjou, R. et al. Disparities in dermatology AI performance on a diverse, curated clinical image set. Sci. Adv. 8, eabq6147 (2022).","journal-title":"Sci. Adv."},{"key":"1419_CR38","doi-asserted-by":"publisher","DOI":"10.1186\/s12916-014-0241-z","volume":"13","author":"GS Collins","year":"2015","unstructured":"Collins, G. S., Reitsma, J. B., Altman, D. G. & Moons, K. G. M. Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis (TRIPOD): the TRIPOD Statement. BMC Med. 13, 1 (2015).","journal-title":"BMC Med."},{"key":"1419_CR39","unstructured":"McLean, K., Knight, S. R., Harrison, E. M. PredictR: An integrated workflow to develop, evaluate, and output predictive models in R, http:\/\/github.com\/kamclean\/predictr (2022)."},{"key":"1419_CR40","doi-asserted-by":"publisher","first-page":"1043","DOI":"10.1007\/s00134-003-1761-8","volume":"29","author":"JE Fischer","year":"2003","unstructured":"Fischer, J. E., Bachmann, L. M. & Jaeschke, R. A readers\u2019 guide to the interpretation of diagnostic test properties: clinical example of sepsis. Intensive Care Med. 29, 1043\u20131051 (2003).","journal-title":"Intensive Care Med."},{"key":"1419_CR41","doi-asserted-by":"crossref","unstructured":"Menzies, T., Kocag\u00fcneli, E., Minku, L., Peters, F. & Turhan, B. in Sharing Data and Models in Software Engineering (eds Tim Menzies et al.) 321\u2013353 (Morgan Kaufmann, 2015).","DOI":"10.1016\/B978-0-12-417295-1.00024-2"},{"key":"1419_CR42","doi-asserted-by":"publisher","first-page":"1285","DOI":"10.1109\/TMI.2016.2528162","volume":"35","author":"HC Shin","year":"2016","unstructured":"Shin, H. C. et al. Deep Convolutional Neural Networks for Computer-Aided Detection: CNN Architectures, Dataset Characteristics and Transfer Learning. IEEE Trans. Med Imaging 35, 1285\u20131298 (2016).","journal-title":"IEEE Trans. Med Imaging"},{"key":"1419_CR43","doi-asserted-by":"publisher","DOI":"10.1016\/j.cmpb.2021.106045","volume":"204","author":"R S\u00e1nchez-Cauce","year":"2021","unstructured":"S\u00e1nchez-Cauce, R., P\u00e9rez-Mart\u00edn, J. & Luque, M. Multi-input convolutional neural network for breast cancer detection using thermal images and clinical data. Comput. Methods Prog. Biomed. 204, 106045 (2021).","journal-title":"Comput. Methods Prog. Biomed."},{"key":"1419_CR44","doi-asserted-by":"crossref","unstructured":"Nguyen, T. & Pernkopf, F. Crackle Detection In Lung Sounds Using Transfer Learning And Multi-Input Convolutional Neural Networks. 43rd Annual International Conference of the IEEE Engineering in Medicine & Biology Society (EMBC). 80\u201383 (IEEE, 2021).","DOI":"10.1109\/EMBC46164.2021.9630577"},{"key":"1419_CR45","unstructured":"National Health Service England. How do I calculate the FTE \/ WTE required for a role? (National Health Service England, 2024)."}],"container-title":["npj Digital Medicine"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01419-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01419-8","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01419-8.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,2,23]],"date-time":"2025-02-23T13:29:28Z","timestamp":1740317368000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.nature.com\/articles\/s41746-024-01419-8"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,2,23]]},"references-count":45,"journal-issue":{"issue":"1","published-online":{"date-parts":[[2025,12]]}},"alternative-id":["1419"],"URL":"https:\/\/doi.org\/10.1038\/s41746-024-01419-8","relation":{},"ISSN":["2398-6352"],"issn-type":[{"value":"2398-6352","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,2,23]]},"assertion":[{"value":"26 July 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"21 December 2024","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"23 February 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"The authors declare no competing interests.","order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Competing interests"}}],"article-number":"121"}}