Detection of Center-Involved Diabetic Macular Edema with Visual Impairment using Multimodal Artificial Intelligence Algorithms

To develop artificial intelligence (AI) models for automated detection of center-involved diabetic macular edema (CI-DME) with visual impairment using color fundus photographs (CFP) and optical coherence tomography (OCT) scans. AI effort using pooled data from multi-center studies. Datasets 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 dataset was from DRCR Retina Network clinical trials, external testing dataset 1 was from the Singapore National Eye Centre, Singapore, and external testing dataset 2 was from the Eye Clinic, IRCCS MultiMedica, Milan, Italy. AI 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 1,007 eyes were used to train and validate the algorithms, and data from 448 eyes were used for testing. Area under the receiver operating characteristic curve (AUC) values. In the primary testing set, the CFP model, OCT model, and multimodal model had AUCs of 0.848 (95% 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 dataset 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 dataset 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. The AI models showed good diagnostic performance for 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 triage and detection of patients who require prompt treatment.