A Novel Multimodal Implementation of a Foundation Artificial Intelligence Model Using Optic Nerve Head Fundus Photographs and OCT Imaging for Glaucoma Detection

Le résumé fourni par la source

Purpose To compare the performance of unimodal and multimodal implementation of the self-supervised learning model RETFound in detecting glaucoma using color fundus photographs (CFP) and optical coherence tomography (OCT) images, and to assess its generalizability across different ethnicities, age groups, and disease severities. Design Evaluation of a diagnostic technology Subjects, Participants, and Controls 14,510 CFPs and 32,640 OCTs from 1,948 eyes of 1,098 participants (60.8% glaucoma, 39.2% healthy) from the Diagnostic Innovations in Glaucoma Study (DIGS) and the African Descent and Glaucoma Evaluation Study (ADAGES) were included. Glaucoma was defined as photograph-based glaucomatous optic neuropathy (GON) with or without repeatable glaucoma visual field damage (GVFD). Methods A multimodal RETFound model was developed using paired CFPs and OCT images. The model was compared to unimodal RETFound models using solely CFP or OCT images. Performance was also stratified by race (Black vs. White), age (<60 vs. ≥60 years), and disease severity (mild vs. moderate-to-severe glaucoma). Main Outcome Measures Diagnostic accuracy of unimodal and multimodal RETFound models using CFP and OCT for detecting glaucoma was assessed using the area under the receiver operating characteristic curve (AUC), precision, and recall. Results The multimodal model for glaucoma detection achieved an AUC of 0.94 (95% CI: 0.91–0.97), significantly outperforming the CFP unimodal model (AUC 0.86 [95% CI: 0.81–0.89], p < 0.001) but not the OCT unimodal model (AUC 0.93 [95% CI: 0.90–0.96], p=0.47). Precision and recall were higher (0.96 and 0.87, respectively) for the multimodal model compared to the CFP model (0.92 and 0.69) across all subgroups. No significant differences based on race or age were found in either unimodal or multimodal glaucoma detection models. All models exhibited better performance in detecting moderate-to-severe glaucoma than mild glaucoma, with significant differences in the unimodal CFP (p = 0.002) and OCT (p = 0.005) models. Conclusions The multimodal RETFound model demonstrated improved diagnostic ability compared to the CFP unimodal model but did not significantly outperform the OCT unimodal model in glaucoma detection. As real-world clinical implementation of a unimodal AI model is easier than a multimodal counterpart, our results suggest unimodal OCT AI models may be sufficient for detecting glaucoma.

Ce résumé expose les affirmations des auteurs. BNTIC ne l’interprète pas comme une validation indépendante des résultats.