{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,21]],"date-time":"2026-05-21T03:12:56Z","timestamp":1779333176208,"version":"3.51.4"},"reference-count":48,"publisher":"Springer Science and Business Media LLC","issue":"9","license":[{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"},{"start":{"date-parts":[[2025,3,31]],"date-time":"2025-03-31T00:00:00Z","timestamp":1743379200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.springernature.com\/gp\/researchers\/text-and-data-mining"}],"content-domain":{"domain":["link.springer.com"],"crossmark-restriction":false},"short-container-title":["Med Biol Eng Comput"],"published-print":{"date-parts":[[2025,9]]},"DOI":"10.1007\/s11517-025-03353-7","type":"journal-article","created":{"date-parts":[[2025,4,1]],"date-time":"2025-04-01T22:23:45Z","timestamp":1743546225000},"page":"2679-2691","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":9,"title":["Automated detection of retinal artery occlusion in fundus photography via self-supervised deep learning and multimodal interpretability using a multimodal AI chatbot"],"prefix":"10.1007","volume":"63","author":[{"given":"Sun Young","family":"Ryu","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Joon Yul","family":"Choi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0890-8614","authenticated-orcid":false,"given":"Tae Keun","family":"Yoo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"297","published-online":{"date-parts":[[2025,3,31]]},"reference":[{"key":"3353_CR1","doi-asserted-by":"publisher","first-page":"2319","DOI":"10.1038\/s41433-024-03029-w","volume":"38","author":"C Chen","year":"2024","unstructured":"Chen C, Singh G, Madike R, Cugati S (2024) Central retinal artery occlusion: a stroke of the eye. Eye 38:2319\u20132326. https:\/\/doi.org\/10.1038\/s41433-024-03029-w","journal-title":"Eye"},{"key":"3353_CR2","doi-asserted-by":"publisher","first-page":"1881","DOI":"10.1016\/j.ophtha.2015.05.044","volume":"122","author":"J Callizo","year":"2015","unstructured":"Callizo J, Feltgen N, Pantenburg S et al (2015) Cardiovascular risk factors in central retinal artery occlusion: results of a prospective and standardized medical examination. Ophthalmology 122:1881\u20131888. https:\/\/doi.org\/10.1016\/j.ophtha.2015.05.044","journal-title":"Ophthalmology"},{"key":"3353_CR3","doi-asserted-by":"publisher","first-page":"105002","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105002","volume":"29","author":"IS Shaikh","year":"2020","unstructured":"Shaikh IS, Elsamna ST, Zarbin MA, Bhagat N (2020) Assessing the risk of stroke development following retinal artery occlusion. J Stroke Cerebrovasc Dis 29:105002. https:\/\/doi.org\/10.1016\/j.jstrokecerebrovasdis.2020.105002","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"3353_CR4","doi-asserted-by":"publisher","first-page":"1110","DOI":"10.1001\/jamaophthalmol.2023.4716","volume":"141","author":"KM Wai","year":"2023","unstructured":"Wai KM, Knapp A, Ludwig CA et al (2023) Risk of stroke, myocardial infarction, and death after retinal artery occlusion. JAMA Ophthalmology 141:1110\u20131116. https:\/\/doi.org\/10.1001\/jamaophthalmol.2023.4716","journal-title":"JAMA Ophthalmology"},{"key":"3353_CR5","doi-asserted-by":"publisher","first-page":"100959","DOI":"10.1016\/j.ajoc.2020.100959","volume":"20","author":"S-N Chen","year":"2020","unstructured":"Chen S-N, Huang J, Hwang J-F (2020) Bilateral retinal artery occlusions with carotid artery occlusions-ocular and cerebral hemodynamic changes. Am J Ophthalmol Case Rep 20:100959. https:\/\/doi.org\/10.1016\/j.ajoc.2020.100959","journal-title":"Am J Ophthalmol Case Rep"},{"key":"3353_CR6","first-page":"1","volume-title":"Diseases of the Retina and Vitreous: Clinical & Surgical Practice and Innovations","author":"E Novais","year":"2024","unstructured":"Novais E, Roisman L, Louzada R (2024) Arterial Retinal Occlusions. In: Ferreira MA (ed) Diseases of the Retina and Vitreous: Clinical & Surgical Practice and Innovations. Springer International Publishing, Cham, pp 1\u201312"},{"key":"3353_CR7","doi-asserted-by":"publisher","first-page":"105828","DOI":"10.1016\/j.jstrokecerebrovasdis.2021.105828","volume":"30","author":"B Mac Grory","year":"2021","unstructured":"Mac Grory B, Schrag M, Poli S et al (2021) Structural and functional imaging of the retina in central retinal artery occlusion \u2013 current approaches and future directions. J Stroke Cerebrovasc Dis 30:105828. https:\/\/doi.org\/10.1016\/j.jstrokecerebrovasdis.2021.105828","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"3353_CR8","doi-asserted-by":"publisher","first-page":"434","DOI":"10.1186\/s12886-023-03176-w","volume":"23","author":"W Fan","year":"2023","unstructured":"Fan W, Huang Y, Zhao Y, Yuan R (2023) Central retinal artery occlusion without cherry-red spots. BMC Ophthalmol 23:434. https:\/\/doi.org\/10.1186\/s12886-023-03176-w","journal-title":"BMC Ophthalmol"},{"key":"3353_CR9","doi-asserted-by":"publisher","first-page":"820","DOI":"10.1016\/j.ajo.2011.05.005","volume":"152","author":"JA Leavitt","year":"2011","unstructured":"Leavitt JA, Larson TA, Hodge DO, Gullerud RE (2011) The incidence of central retinal artery occlusion in Olmsted county. Minnesota Am J Ophthalmol 152:820. https:\/\/doi.org\/10.1016\/j.ajo.2011.05.005","journal-title":"Minnesota Am J Ophthalmol"},{"key":"3353_CR10","doi-asserted-by":"publisher","first-page":"633","DOI":"10.1007\/s11517-021-02487-8","volume":"60","author":"WX Lim","year":"2022","unstructured":"Lim WX, Chen Z, Ahmed A (2022) The adoption of deep learning interpretability techniques on diabetic retinopathy analysis: a review. Med Biol Eng Compu 60:633. https:\/\/doi.org\/10.1007\/s11517-021-02487-8","journal-title":"Med Biol Eng Compu"},{"key":"3353_CR11","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00811-0","volume":"6","author":"S-C Huang","year":"2023","unstructured":"Huang S-C, Pareek A, Jensen M et al (2023) Self-supervised learning for medical image classification: a systematic review and implementation guidelines. npj Digit Med 6:1\u201316. https:\/\/doi.org\/10.1038\/s41746-023-00811-0","journal-title":"npj Digit Med"},{"key":"3353_CR12","doi-asserted-by":"crossref","unstructured":"Kim H, Seo C, Cho Y, Yoo TK (2025) Patient-Level Contrastive Learning for\u00a0Enhanced Biomarker Prediction in\u00a0Retinal Imaging. In: Bhattarai B, Ali S, Rau A, et al (eds) Data Engineering in Medical Imaging. Springer Nature Switzerland, Cham, pp 125\u2013133","DOI":"10.1007\/978-3-031-73748-0_13"},{"key":"3353_CR13","doi-asserted-by":"publisher","first-page":"1618","DOI":"10.1007\/s10278-024-00975-5","volume":"37","author":"K Imagawa","year":"2024","unstructured":"Imagawa K, Shiomoto K (2024) Evaluation of effectiveness of self-supervised learning in chest X-ray imaging to reduce annotated images. J Digit Imaging Inform Med 37:1618\u20131624. https:\/\/doi.org\/10.1007\/s10278-024-00975-5","journal-title":"J Digit Imaging Inform Med"},{"key":"3353_CR14","doi-asserted-by":"publisher","first-page":"1090","DOI":"10.1039\/D4AY01970A","volume":"17","author":"R Zhao","year":"2025","unstructured":"Zhao R, Li W, Xu J et al (2025) A CNN-based self-supervised learning framework for small-sample near-infrared spectroscopy classification. Anal Methods 17:1090\u20131100. https:\/\/doi.org\/10.1039\/D4AY01970A","journal-title":"Anal Methods"},{"key":"3353_CR15","doi-asserted-by":"publisher","first-page":"337","DOI":"10.21037\/atm-23-1598","volume":"11","author":"JY Choi","year":"2023","unstructured":"Choi JY, Yoo TK (2023) New era after ChatGPT in ophthalmology: advances from data-based decision support to patient-centered generative artificial intelligence. Ann Transl Med 11:337. https:\/\/doi.org\/10.21037\/atm-23-1598","journal-title":"Ann Transl Med"},{"key":"3353_CR16","doi-asserted-by":"publisher","first-page":"488","DOI":"10.18502\/jovr.v19i4.16559","volume":"19","author":"HJ Yu","year":"2024","unstructured":"Yu HJ, Choi S, Guiseppi R, Banaee T (2024) Retinal artery occlusion: a review of current management practices. J Ophthalmic Vis Res 19:488\u2013507. https:\/\/doi.org\/10.18502\/jovr.v19i4.16559","journal-title":"J Ophthalmic Vis Res"},{"key":"3353_CR17","doi-asserted-by":"publisher","unstructured":"Beuse A, Wenzel DA, Spitzer MS, et al (2024) Automated detection of central retinal artery occlusion using OCT imaging via explainable deep learning. Ophthalmol Sci 100630. https:\/\/doi.org\/10.1016\/j.xops.2024.100630","DOI":"10.1016\/j.xops.2024.100630"},{"key":"3353_CR18","doi-asserted-by":"publisher","first-page":"725","DOI":"10.1016\/j.bbe.2023.10.005","volume":"43","author":"EY Choi","year":"2023","unstructured":"Choi EY, Han SH, Ryu IH et al (2023) Automated detection of crystalline retinopathy via fundus photography using multistage generative adversarial networks. Biocybernetics and Biomed Eng 43:725\u2013735","journal-title":"Biocybernetics and Biomed Eng"},{"key":"3353_CR19","doi-asserted-by":"publisher","first-page":"401","DOI":"10.1007\/s11517-021-02321-1","volume":"59","author":"TK Yoo","year":"2021","unstructured":"Yoo TK, Choi JY, Kim HK (2021) Feasibility study to improve deep learning in OCT diagnosis of rare retinal diseases with few-shot classification. Med Biol Eng Comput 59:401\u2013415. https:\/\/doi.org\/10.1007\/s11517-021-02321-1","journal-title":"Med Biol Eng Comput"},{"key":"3353_CR20","doi-asserted-by":"publisher","first-page":"14","DOI":"10.3390\/data6020014","volume":"6","author":"S Pachade","year":"2021","unstructured":"Pachade S, Porwal P, Thulkar D et al (2021) Retinal Fundus Multi-Disease Image Dataset (RFMiD): a dataset for multi-disease detection research. Data 6:14. https:\/\/doi.org\/10.3390\/data6020014","journal-title":"Data"},{"key":"3353_CR21","doi-asserted-by":"publisher","first-page":"4828","DOI":"10.1038\/s41467-021-25138-w","volume":"12","author":"L-P Cen","year":"2021","unstructured":"Cen L-P, Ji J, Lin J-W et al (2021) Automatic detection of 39 fundus diseases and conditions in retinal photographs using deep neural networks. Nat Commun 12:4828. https:\/\/doi.org\/10.1038\/s41467-021-25138-w","journal-title":"Nat Commun"},{"key":"3353_CR22","doi-asserted-by":"publisher","first-page":"1122","DOI":"10.1016\/j.cell.2018.02.010","volume":"172","author":"DS Kermany","year":"2018","unstructured":"Kermany DS, Goldbaum M, Cai W et al (2018) Identifying medical diagnoses and treatable diseases by image-based deep learning. Cell 172:1122-1131.e9. https:\/\/doi.org\/10.1016\/j.cell.2018.02.010","journal-title":"Cell"},{"key":"3353_CR23","doi-asserted-by":"publisher","first-page":"810","DOI":"10.1016\/j.neunet.2023.09.001","volume":"167","author":"W Chen","year":"2023","unstructured":"Chen W, Li C, Chen D, Luo X (2023) A knowledge-based learning framework for self-supervised pre-training towards enhanced recognition of biomedical microscopy images. Neural Netw 167:810\u2013826. https:\/\/doi.org\/10.1016\/j.neunet.2023.09.001","journal-title":"Neural Netw"},{"key":"3353_CR24","doi-asserted-by":"publisher","first-page":"122807","DOI":"10.1016\/j.eswa.2023.122807","volume":"242","author":"Z Zhao","year":"2024","unstructured":"Zhao Z, Alzubaidi L, Zhang J et al (2024) A comparison review of transfer learning and self-supervised learning: definitions, applications, advantages and limitations. Expert Syst Appl 242:122807. https:\/\/doi.org\/10.1016\/j.eswa.2023.122807","journal-title":"Expert Syst Appl"},{"key":"3353_CR25","doi-asserted-by":"publisher","first-page":"5131","DOI":"10.1038\/s41467-020-18918-3","volume":"11","author":"Y Gao","year":"2020","unstructured":"Gao Y, Cui Y (2020) Deep transfer learning for reducing health care disparities arising from biomedical data inequality. Nat Commun 11:5131. https:\/\/doi.org\/10.1038\/s41467-020-18918-3","journal-title":"Nat Commun"},{"key":"3353_CR26","doi-asserted-by":"publisher","first-page":"305","DOI":"10.1007\/s40123-023-00842-6","volume":"13","author":"R Jain","year":"2024","unstructured":"Jain R, Yoo TK, Ryu IH et al (2024) Deep transfer learning for ethnically distinct populations: prediction of refractive error using optical coherence tomography. Ophthalmol Ther 13:305\u2013319. https:\/\/doi.org\/10.1007\/s40123-023-00842-6","journal-title":"Ophthalmol Ther"},{"key":"3353_CR27","doi-asserted-by":"publisher","first-page":"117546","DOI":"10.1109\/ACCESS.2023.3326528","volume":"11","author":"W Nazih","year":"2023","unstructured":"Nazih W, Aseeri AO, Atallah OY, El-Sappagh S (2023) Vision transformer model for predicting the severity of diabetic retinopathy in fundus photography-based retina images. IEEE Access 11:117546\u2013117561. https:\/\/doi.org\/10.1109\/ACCESS.2023.3326528","journal-title":"IEEE Access"},{"key":"3353_CR28","doi-asserted-by":"publisher","first-page":"30554","DOI":"10.1038\/s41598-024-81132-4","volume":"14","author":"L Arora","year":"2024","unstructured":"Arora L, Singh SK, Kumar S et al (2024) Ensemble deep learning and EfficientNet for accurate diagnosis of diabetic retinopathy. Sci Rep 14:30554. https:\/\/doi.org\/10.1038\/s41598-024-81132-4","journal-title":"Sci Rep"},{"key":"3353_CR29","doi-asserted-by":"publisher","unstructured":"Wu M, Lu Y, Hong X, et al (2022) Classification of dry and wet macular degeneration based on the ConvNeXT model. Front Comput Neurosci 16. https:\/\/doi.org\/10.3389\/fncom.2022.1079155","DOI":"10.3389\/fncom.2022.1079155"},{"key":"3353_CR30","doi-asserted-by":"publisher","first-page":"125553","DOI":"10.1016\/j.eswa.2024.125553","volume":"261","author":"T Gulsoy","year":"2025","unstructured":"Gulsoy T, Baykal Kablan E (2025) FocalNeXt: A ConvNeXt augmented FocalNet architecture for lung cancer classification from CT-scan images. Expert Syst Appl 261:125553. https:\/\/doi.org\/10.1016\/j.eswa.2024.125553","journal-title":"Expert Syst Appl"},{"key":"3353_CR31","doi-asserted-by":"publisher","first-page":"1762","DOI":"10.1109\/JBHI.2019.2949601","volume":"24","author":"T He","year":"2020","unstructured":"He T, Guo J, Chen N et al (2020) MediMLP: Using Grad-CAM to extract crucial variables for lung cancer postoperative complication prediction. IEEE J Biomed Health Inform 24:1762\u20131771. https:\/\/doi.org\/10.1109\/JBHI.2019.2949601","journal-title":"IEEE J Biomed Health Inform"},{"key":"3353_CR32","doi-asserted-by":"publisher","first-page":"1231","DOI":"10.1016\/j.ajo.2014.01.033","volume":"157","author":"J Lee","year":"2014","unstructured":"Lee J, Kim SW, Lee SC et al (2014) Co-occurrence of acute retinal artery occlusion and acute ischemic stroke: diffusion-weighted magnetic resonance imaging study. Am J Ophthalmol 157:1231\u20131238. https:\/\/doi.org\/10.1016\/j.ajo.2014.01.033","journal-title":"Am J Ophthalmol"},{"key":"3353_CR33","doi-asserted-by":"publisher","first-page":"105531","DOI":"10.1016\/j.jstrokecerebrovasdis.2020.105531","volume":"30","author":"KE Lee","year":"2021","unstructured":"Lee KE, Tschoe C, Coffman SA et al (2021) Management of acute central retinal artery occlusion, a \u201cRetinal Stroke\u201d: an institutional series and literature review. J Stroke Cerebrovasc Dis 30:105531. https:\/\/doi.org\/10.1016\/j.jstrokecerebrovasdis.2020.105531","journal-title":"J Stroke Cerebrovasc Dis"},{"key":"3353_CR34","doi-asserted-by":"publisher","first-page":"147","DOI":"10.1186\/s12886-019-1152-8","volume":"19","author":"S Yang","year":"2019","unstructured":"Yang S, Liu X, Li H et al (2019) Optical coherence tomography angiography characteristics of acute retinal arterial occlusion. BMC Ophthalmol 19:147. https:\/\/doi.org\/10.1186\/s12886-019-1152-8","journal-title":"BMC Ophthalmol"},{"key":"3353_CR35","doi-asserted-by":"publisher","first-page":"e0187336","DOI":"10.1371\/journal.pone.0187336","volume":"12","author":"JY Choi","year":"2017","unstructured":"Choi JY, Yoo TK, Seo JG et al (2017) Multi-categorical deep learning neural network to classify retinal images: a pilot study employing small database. PLoS ONE 12:e0187336. https:\/\/doi.org\/10.1371\/journal.pone.0187336","journal-title":"PLoS ONE"},{"key":"3353_CR36","doi-asserted-by":"publisher","first-page":"4589","DOI":"10.1167\/iovs.16-19887","volume":"57","author":"KH Cho","year":"2016","unstructured":"Cho KH, Ahn SJ, Cho JH et al (2016) The characteristics of retinal emboli and its association with vascular reperfusion in retinal artery occlusion. Invest Ophthalmol Vis Sci 57:4589\u20134598. https:\/\/doi.org\/10.1167\/iovs.16-19887","journal-title":"Invest Ophthalmol Vis Sci"},{"key":"3353_CR37","doi-asserted-by":"publisher","first-page":"116","DOI":"10.1016\/j.oret.2021.06.010","volume":"6","author":"B Yellapragada","year":"2022","unstructured":"Yellapragada B, Hornauer S, Snyder K et al (2022) Self-supervised feature learning and phenotyping for assessing age-related macular degeneration using retinal fundus images. Ophthalmol Retina 6:116\u2013129. https:\/\/doi.org\/10.1016\/j.oret.2021.06.010","journal-title":"Ophthalmol Retina"},{"key":"3353_CR38","doi-asserted-by":"publisher","first-page":"584","DOI":"10.1038\/s41591-023-02702-z","volume":"30","author":"L Dai","year":"2024","unstructured":"Dai L, Sheng B, Chen T et al (2024) A deep learning system for predicting time to progression of diabetic retinopathy. Nat Med 30:584\u2013594. https:\/\/doi.org\/10.1038\/s41591-023-02702-z","journal-title":"Nat Med"},{"key":"3353_CR39","doi-asserted-by":"publisher","first-page":"1089","DOI":"10.3390\/bioengineering10091089","volume":"10","author":"TD Nguyen","year":"2023","unstructured":"Nguyen TD, Le D-T, Bum J et al (2023) Self-FI: self-supervised learning for disease diagnosis in fundus images. Bioengineering 10:1089. https:\/\/doi.org\/10.3390\/bioengineering10091089","journal-title":"Bioengineering"},{"key":"3353_CR40","doi-asserted-by":"publisher","first-page":"193907","DOI":"10.1109\/ACCESS.2020.3031549","volume":"8","author":"PH Le-Khac","year":"2020","unstructured":"Le-Khac PH, Healy G, Smeaton AF (2020) Contrastive representation learning: a framework and review. IEEE Access 8:193907\u2013193934. https:\/\/doi.org\/10.1109\/ACCESS.2020.3031549","journal-title":"IEEE Access"},{"key":"3353_CR41","doi-asserted-by":"publisher","first-page":"3266","DOI":"10.3390\/diagnostics13203266","volume":"13","author":"A Demircio\u011flu","year":"2023","unstructured":"Demircio\u011flu A (2023) Deep features from pretrained networks do not outperform hand-crafted features in radiomics. Diagnostics 13:3266. https:\/\/doi.org\/10.3390\/diagnostics13203266","journal-title":"Diagnostics"},{"key":"3353_CR42","doi-asserted-by":"publisher","DOI":"10.1007\/s11517-024-03182-0","author":"JY Choi","year":"2024","unstructured":"Choi JY, Yoo TK (2024) Development of a novel scoring system for glaucoma risk based on demographic and laboratory factors using ChatGPT-4. Med Biol Eng Comput. https:\/\/doi.org\/10.1007\/s11517-024-03182-0","journal-title":"Med Biol Eng Comput"},{"key":"3353_CR43","doi-asserted-by":"publisher","first-page":"50","DOI":"10.3390\/biomedinformatics4010004","volume":"4","author":"H Demirhan","year":"2024","unstructured":"Demirhan H, Zadrozny W (2024) Survey of multimodal medical question answering. BioMedInformatics 4:50\u201374. https:\/\/doi.org\/10.3390\/biomedinformatics4010004","journal-title":"BioMedInformatics"},{"key":"3353_CR44","doi-asserted-by":"publisher","first-page":"106735","DOI":"10.1016\/j.cmpb.2022.106735","volume":"219","author":"TK Yoo","year":"2022","unstructured":"Yoo TK, Ryu IH, Kim JK et al (2022) A deep learning approach for detection of shallow anterior chamber depth based on the hidden features of fundus photographs. Comput Methods Programs Biomed 219:106735. https:\/\/doi.org\/10.1016\/j.cmpb.2022.106735","journal-title":"Comput Methods Programs Biomed"},{"key":"3353_CR45","doi-asserted-by":"publisher","DOI":"10.1016\/j.ophtha.2020.06.036","author":"J Chang","year":"2020","unstructured":"Chang J, Lee J, Ha A et al (2020) Explaining the rationale of deep learning glaucoma decisions with adversarial examples. Ophthalmology. https:\/\/doi.org\/10.1016\/j.ophtha.2020.06.036","journal-title":"Ophthalmology"},{"key":"3353_CR46","doi-asserted-by":"publisher","first-page":"6","DOI":"10.1167\/tvst.9.2.6","volume":"9","author":"SK Wagner","year":"2020","unstructured":"Wagner SK, Fu DJ, Faes L et al (2020) Insights into systemic disease through retinal imaging-based oculomics. Trans Vis Sci Tech 9:6\u20136. https:\/\/doi.org\/10.1167\/tvst.9.2.6","journal-title":"Trans Vis Sci Tech"},{"key":"3353_CR47","doi-asserted-by":"publisher","first-page":"1","DOI":"10.1038\/s41746-023-00748-4","volume":"6","author":"YC Lee","year":"2023","unstructured":"Lee YC, Cha J, Shim I et al (2023) Multimodal deep learning of fundus abnormalities and traditional risk factors for cardiovascular risk prediction. npg Digit Med 6:1\u201310. https:\/\/doi.org\/10.1038\/s41746-023-00748-4","journal-title":"npg Digit Med"},{"key":"3353_CR48","doi-asserted-by":"publisher","first-page":"659","DOI":"10.1007\/s13167-024-00378-0","volume":"15","author":"JY Choi","year":"2024","unstructured":"Choi JY, Han E, Yoo TK (2024) Application of ChatGPT-4 to oculomics: a cost-effective osteoporosis risk assessment to enhance management as a proof-of-principles model in 3PM. EPMA Journal 15:659\u2013676. https:\/\/doi.org\/10.1007\/s13167-024-00378-0","journal-title":"EPMA Journal"}],"container-title":["Medical & Biological Engineering & Computing"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03353-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/article\/10.1007\/s11517-025-03353-7\/fulltext.html","content-type":"text\/html","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/link.springer.com\/content\/pdf\/10.1007\/s11517-025-03353-7.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,9,6]],"date-time":"2025-09-06T08:26:01Z","timestamp":1757147161000},"score":1,"resource":{"primary":{"URL":"https:\/\/link.springer.com\/10.1007\/s11517-025-03353-7"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,3,31]]},"references-count":48,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["3353"],"URL":"https:\/\/doi.org\/10.1007\/s11517-025-03353-7","relation":{},"ISSN":["0140-0118","1741-0444"],"issn-type":[{"value":"0140-0118","type":"print"},{"value":"1741-0444","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,3,31]]},"assertion":[{"value":"12 November 2024","order":1,"name":"received","label":"Received","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"18 March 2025","order":2,"name":"accepted","label":"Accepted","group":{"name":"ArticleHistory","label":"Article History"}},{"value":"31 March 2025","order":3,"name":"first_online","label":"First Online","group":{"name":"ArticleHistory","label":"Article History"}},{"order":1,"name":"Ethics","group":{"name":"EthicsHeading","label":"Declarations"}},{"value":"All procedures complied with the ethical standards of the institutional and national research committees, as well as the 1964 Declaration of Helsinki and its later amendments or comparable ethical guidelines. As this study utilized publicly accessible, de-identified, web-based data, ethics committee approval was not required, and no clinical data from human participants were obtained.","order":2,"name":"Ethics","group":{"name":"EthicsHeading","label":"Ethics approval"}},{"value":"Not applicable.","order":3,"name":"Ethics","group":{"name":"EthicsHeading","label":"Consent to participate"}},{"value":"The authors declare no competing interests.","order":4,"name":"Ethics","group":{"name":"EthicsHeading","label":"Conflict of interest"}}]}}