ArticleOphthalmology. Retina2025
Detection of Center-Involved Diabetic Macular Edema With Visual Impairment Using Multimodal Artificial Intelligence Algorithms.
Article in Ophthalmology. Retina, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.
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Who cites it
5 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Artificial intelligence in diabetic retinopathy: from automated screening to risk-stratified care.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Advances in diagnosing and treating diabetic retinopathy.Nature reviews. Endocrinology · 2026Review
- Deep Learning Prediction of Retinal Thickness from Near-Infrared Fundus Photography: Toward Decentralized Quantitative Assessment of Diabetic Macular Edema.Journal of personalized medicine · 2026Article
- Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning.Translational vision science & technology · 2026Article
Corrections and comments
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Authors and funding
15 authors.
Funding
Abstract
purposeTo develop artificial intelligence (AI) models for automated detection of center-involved diabetic macular edema (CI-DME) with visual impairment using color fundus photographs (CFPs) and OCT scans.
designArtificial intelligence effort using pooled data from multicenter studies.
participantsData sets consisted of diabetic participants with or without CI-DME, who had CFP, OCT, and best-corrected visual acuity (BCVA) obtained after manifest refraction. The development data set was from DRCR Retina Network clinical trials, external testing data set 1 was from the Singapore National Eye Centre, Singapore, and external testing data set 2 was from the Eye Clinic, IRCCS MultiMedica, Milan, Italy.
methodsArtificial intelligence models were trained to detect CI-DME, visual impairment (BCVA 20/32 or worse), and CI-DME with visual impairment, using CFPs alone, OCTs alone, and both CFPs and OCTs together (multimodal). Data from 1007 eyes were used to train and validate the algorithms, and data from 448 eyes were used for testing.
main outcome measuresArea under the receiver operating characteristic curve (AUC) values.
resultsIn the primary testing set, the CFP model, OCT model, and multimodal model had AUCs of 0.848 (95% confidence interval [CI], 0.787-0.900), 0.913 (95% CI, 0.870-0.947), and 0.939 (95% CI, 0.906-0.964), respectively, for detection of CI-DME with visual impairment. In external testing data set 1, the CFP, OCT, and multimodal models had AUCs of 0.756 (95% CI, 0.624-0.870), 0.949 (95% CI, 0.889-0.989), and 0.917 (95% CI, 0.837-0.979), respectively, for detection of CI-DME with visual impairment. In external testing data set 2, the CFP, OCT, and multimodal models had AUCs of 0.881 (95% CI, 0.822-0.940), 0.828 (95% CI, 0.749-0.905), and 0.907 (95% CI, 0.852-0.952), respectively, for detection of CI-DME with visual impairment.
conclusionsThe AI models showed good diagnostic performance for the detection of CI-DME with visual impairment. The multimodal (CFP and OCT) model did not offer additional benefit over the OCT model alone. If validated in prospective studies, these AI models could potentially help to improve the triage and detection of patients who require prompt treatment. FINANCIAL DISCLOSURE(S): Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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