{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,16]],"date-time":"2026-10-16T18:04:34Z","timestamp":1792173874735,"version":"4.4.0"},"reference-count":40,"publisher":"American Diabetes Association","issue":"9","license":[{"start":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T00:00:00Z","timestamp":1751328000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/www.diabetesjournals.org\/journals\/pages\/license"}],"funder":[{"name":"Ministry of Health & Welfare, South Korea","award":["HE23C002800"],"award-info":[{"award-number":["HE23C002800"]}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["IITP-2024-RS-2024-00438239"],"award-info":[{"award-number":["IITP-2024-RS-2024-00438239"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100010418","name":"Institute for Information and Communications Technology Promotion","doi-asserted-by":"publisher","award":["RS-2024-00509257"],"award-info":[{"award-number":["RS-2024-00509257"]}],"id":[{"id":"10.13039\/501100010418","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2023-00262002"],"award-info":[{"award-number":["RS-2023-00262002"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/501100003725","name":"National Research Foundation of Korea","doi-asserted-by":"publisher","award":["RS-2024-00460379"],"award-info":[{"award-number":["RS-2024-00460379"]}],"id":[{"id":"10.13039\/501100003725","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["diabetesjournals.org"],"crossmark-restriction":true},"short-container-title":[],"published-print":{"date-parts":[[2025,9,1]]},"abstract":"<jats:sec>\n                    <jats:title>OBJECTIVE<\/jats:title>\n                    <jats:p>To develop a multimodal model to predict chronic kidney disease (CKD) in patients with type 2 diabetes mellitus (T2DM), given the limited research on this integrative approach.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESEARCH DESIGN AND METHODS<\/jats:title>\n                    <jats:p>We obtained multimodal data sets from Kyung Hee University Medical Center (n = 7,028; discovery cohort) for training and internal validation and UK Biobank (n = 1,544; validation cohort) for external validation. CKD was defined based on ICD-9 and ICD-10 codes and\/or estimated glomerular filtration rate (eGFR) \u226460 mL\/min\/1.73 m2. We ensembled various deep learning models and interpreted their predictions using explainable artificial intelligence (AI) methods, including Shapley additive explanation values (SHAP) and gradient-weighted class activation mapping (Grad-CAM). Subsequently, we investigated the potential association between the model probability and vascular complications.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>RESULTS<\/jats:title>\n                    <jats:p>The multimodal model, which ensembles visual geometry group 16 and deep neural network, presented high performance in predicting CKD, with area under the receiver operating characteristic curve of 0.880 (95% CI 0.806\u20130.954) in the discovery cohort and 0.722 in the validation cohort. SHAP and Grad-CAM highlighted key predictors, including eGFR and optic disc, respectively. The model probability was associated with an increased risk of macrovascular complications (tertile 1 [T1]: adjusted hazard ratio, 1.42 [95% CI 1.06\u20131.90]; T2: 1.59 [1.17\u20132.16]; T3: 1.64 [1.20\u20132.26]) and microvascular complications (T3: 1.30 [1.02\u20131.67]).<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>CONCLUSIONS<\/jats:title>\n                    <jats:p>Our multimodal AI model integrates fundus images and clinical data from binational cohorts to predict the risk of new-onset CKD within 5 years and associated vascular complications in patients with T2DM.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.2337\/dc25-0355","type":"journal-article","created":{"date-parts":[[2025,7,1]],"date-time":"2025-07-01T09:30:48Z","timestamp":1751362248000},"page":"1562-1570","update-policy":"https:\/\/doi.org\/10.2337\/ada-journal-policies","source":"Crossref","is-referenced-by-count":13,"title":["A Multimodal Predictive Model for Chronic Kidney Disease and Its Association With Vascular Complications in Patients With Type 2 Diabetes: Model Development and Validation Study in South Korea and the U.K."],"prefix":"10.2337","volume":"48","author":[{"given":"Jaehyeong","family":"Cho","sequence":"first","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Medicine, CHA University School of Medicine, Seongnam 2 , South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Selin","family":"Woo","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Seung Ha","family":"Hwang","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Biomedical Engineering, Kyung Hee University 3 , Yongin, South Korea"},{"name":"Department of Electronics and Information Convergence Engineering, Kyung Hee University 4 , Yongin, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Soeun","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Precision Medicine, Kyung Hee University College of Medicine 5 , Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hayeon","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Biomedical Engineering, Kyung Hee University 3 , Yongin, South Korea"},{"name":"Department of Electronics and Information Convergence Engineering, Kyung Hee University 4 , Yongin, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jiyoung","family":"Hwang","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Medicine, Kyung Hee University College of Medicine 6 , Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jaewon","family":"Kim","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Medicine, Kyung Hee University College of Medicine 6 , Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Min Seo","family":"Kim","sequence":"additional","affiliation":[{"name":"Medical and Population Genetics and Cardiovascular Disease Initiative, Broad Institute of MIT and Harvard 7 , Cambridge, MA"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Lee","family":"Smith","sequence":"additional","affiliation":[{"name":"Centre for Health, Performance and Wellbeing, Anglia Ruskin University 8 , Cambridge, U.K."},{"name":"Department of Public Health, Faculty of Medicine, Biruni University 9 , Istanbul, Turkey"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Sooji","family":"Lee","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South Korea"},{"name":"Department of Medicine, Kyung Hee University College of Medicine 6 , Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jinseok","family":"Lee","sequence":"additional","affiliation":[{"name":"Department of Biomedical Engineering, Kyung Hee University 3 , Yongin, South Korea"},{"name":"Department of Electronics and Information Convergence Engineering, Kyung Hee University 4 , Yongin, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Hong-Hee","family":"Won","sequence":"additional","affiliation":[{"name":"Department of Digital Health, Samsung Advanced Institute for Health Sciences and Technology, Sungkyunkwan University 10 , Samsung Medical Center, Seoul, South Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0119-5818","authenticated-orcid":false,"given":"Sang Youl","family":"Rhee","sequence":"additional","affiliation":[{"name":"Center for Digital Health, Medical Science Research Institute, Kyung Hee University College of Medicine 1 , Seoul, South 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