Artificial Intelligence Support Improves Diagnosis Accuracy in Anterior Segment Eye Diseases

Abstract

CorneAI, a deep learning model designed for diagnosing cataracts and corneal diseases, was assessed for its impact on ophthalmologists' diagnostic accuracy. In the study, 40 ophthalmologists (20 specialists and 20 residents) classified 100 images, including iPhone 13 Pro photos (50 images) and diffuser slit-lamp photos (50 images), into nine categories (normal condition, infectious keratitis, immunological keratitis, corneal scar, corneal deposit, bullous keratopathy, ocular surface tumor, cataract/intraocular lens opacity, and primary angle-closure glaucoma). The iPhone and slit-lamp images represented the same cases. After initially answering without CorneAI, the same ophthalmologists responded to the same cases with CorneAI 2–4 weeks later. With CorneAI's support, the overall accuracy of ophthalmologists increased significantly from 79.2–88.8% (P < 0.001). Specialists' accuracy rose from 82.8–90.0%, and residents' from 75.6–86.2% (P < 0.001). Smartphone image accuracy improved from 78.7–85.5% and slit-lamp image accuracy from 81.2–90.6% (both, P < 0.001). In this study, CorneAI's own accuracy was 86%, but its support enhanced ophthalmologists' accuracy beyond the CorneAI's baseline. This study demonstrated that CorneAI, despite being trained on diffuser slit-lamp images, effectively improved diagnostic accuracy, even with smartphone images.