Ophthalmology. Retina · 2025
Detection of Center-Involved Diabetic Macular Edema With Visual Impairment Using Multimodal Artificial Intelligence Algorithms.
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Tan Tien-En, Ng Yi Pin, Calhoun Claire, Chaung Jia Quan, Yao Jie, Wang Yan, Zhen Liangli, Xu Xinxing, Liu Yong, Goh Rick S M, Piccoli Gabriele, Vujosevic Stela, Tan Gavin S W, Sun Jennifer K, Ting Daniel S W
Аннотация
PURPOSE: To 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.
DESIGN: Artificial intelligence effort using pooled data from multicenter studies.
PARTICIPANTS: Data 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.
METHODS: Artificial 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 MEASURES: Area under the receiver operating characteristic curve (AUC) values.
RESULTS: In 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.
CONCLUSIONS: The 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.
DESIGN: Artificial intelligence effort using pooled data from multicenter studies.
PARTICIPANTS: Data 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.
METHODS: Artificial 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 MEASURES: Area under the receiver operating characteristic curve (AUC) values.
RESULTS: In 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.
CONCLUSIONS: The 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.