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

Purpose: To compare the performance of unimodal and multimodal implementation of the self-supervised learning model RETFound in detecting glaucoma using color fundus photographs (CFPs) and 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: Fourteen thousand five hundred ten CFPs and 32 640 OCTs from 1948 eyes of 1098 participants (60.8% glaucoma, 39.2% healthy) from the Diagnostic Innovations in Glaucoma Study and the African Descent and Glaucoma Evaluation Study were included. Glaucoma was defined as photograph-based glaucomatous optic neuropathy with or without repeatable glaucoma visual field damage. 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. Conclusions: The multimodal RETFound model demonstrated improved diagnostic ability compared with the CFP unimodal model but did not significantly outperform the OCT unimodal model in glaucoma detection. As clinical implementation of a unimodal artificial intelligence (AI) model is easier than a multimodal counterpart, our results suggest unimodal OCT AI models may be sufficient for detecting glaucoma. Financial Disclosures: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.