{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,10,27]],"date-time":"2026-10-27T03:34:53Z","timestamp":1793072093993,"version":"4.5.0"},"reference-count":37,"publisher":"Wiley","issue":"10","license":[{"start":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T00:00:00Z","timestamp":1792368000000},"content-version":"vor","delay-in-days":18,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"},{"start":{"date-parts":[[2026,10,1]],"date-time":"2026-10-01T00:00:00Z","timestamp":1790812800000},"content-version":"tdm","delay-in-days":0,"URL":"http:\/\/doi.wiley.com\/10.1002\/tdm_license_1.1"}],"funder":[{"DOI":"10.13039\/501100000361","name":"Diabetes UK","doi-asserted-by":"publisher","award":["23\/0006601"],"award-info":[{"award-number":["23\/0006601"]}],"id":[{"id":"10.13039\/501100000361","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Journal of Diabetes"],"published-print":{"date-parts":[[2026,10]]},"abstract":"<jats:title>ABSTRACT<\/jats:title>\n                  <jats:sec>\n                    <jats:title>Aim<\/jats:title>\n                    <jats:p>This study aimed to determine the effectiveness of deep learning models for detecting and predicting diabetes related kidney disease (DRKD) using two\u2010dimensional retinal images.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Methods<\/jats:title>\n                    <jats:p>A systematic review was conducted across eight databases from inception to July 14, 2026. Eligible cross\u2010sectional and longitudinal studies assessed deep learning models for DRKD detection or prediction using retinal images alone, clinical data alone, or hybrid models. Data on study design, model development, validation, and performance metrics were extracted, with study quality appraised using QUADAS\u20102 and post hoc, PROBAST+AI. Results were synthesized narratively, with random effects meta analysis for hybrid models.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Results<\/jats:title>\n                    <jats:p>Of 367 studies, six met inclusion criteria. Three cross\u2010sectional studies of hybrid models contributed to the meta\u2010analysis for DRKD detection, yielding pooled AUC 0.88, sensitivity 0.80, and specificity 0.81, though heterogeneity was high (I2 93.7%\u201399.8%). Three further studies were synthesized narratively rather than pooled: two cross\u2010sectional studies used more severe or narrower CKD case definitions (AUC 0.86 and 0.96 respectively), and only one longitudinal study predicted the 5\u2010year risk of DRKD (AUC 0.81, sensitivity 0.70, and specificity 0.77). External validation showed hybrid models consistently outperformed retinal\u2010only (\u0394AUC 0.03\u20130.11), but their advantage over clinical\u2010only models was inconsistent.<\/jats:p>\n                  <\/jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusion<\/jats:title>\n                    <jats:p>Deep learning models may be useful for detecting and predicting DRKD from retinal images, with or without clinical data. However, the small number of heterogenous studies restricts generalizability. Future work should prioritize robust model development, and validation using large, diverse populations.<\/jats:p>\n                  <\/jats:sec>","DOI":"10.1111\/1753-0407.70271","type":"journal-article","created":{"date-parts":[[2026,10,19]],"date-time":"2026-10-19T09:16:14Z","timestamp":1792401374000},"update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Detection and Risk Prediction of Diabetes Related Kidney Disease Using Retinal Images: A Systematic Review and Meta\u2010Analysis"],"prefix":"10.1111","volume":"18","author":[{"ORCID":"https:\/\/orcid.org\/0009-0005-4295-6443","authenticated-orcid":false,"given":"Samuel Ato Gyasi","family":"Otabir","sequence":"first","affiliation":[{"name":"Centre for Intelligent Healthcare Coventry University  Coventry UK"},{"name":"Institute for Physical Activity and Nutrition, School of Exercise and Nutrition Sciences Deakin University  Melbourne Victoria 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