OBUSight: Clinically Aligned Generative AI for Ophthalmic Ultrasound Interpretation and Diagnosis

Abstract

Ocular B-scan ultrasonography (OBU), widely used for diagnosing posterior segment ocular disorders, poses unique challenges for ophthalmologists in image interpretation. In this study, a clinically aligned generative artificial intelligence (AI) model, OBUSight, was proposed to jointly generate reports and diagnose diseases for comprehensive OBU image interpretation. OBUSight was trained and validated on a large multi-center OBU dataset consisting of 39 654 images and 17 586 corresponding reports from 11 381 patients. By evaluating the quality of generated reports using natural language generation (NLG) metrics and clinical efficacy (CE) metrics, OBUSight outperformed eight state-of-the-art models and demonstrated robust performance across multi-center and multimorbidity validation datasets. The expert rating further indicated that OBUSight can provide clinically aligned reports without major corrections. The ancillary role of OBUSight in enhancing diagnostic efficiency was evaluated by providing ophthalmologists, residents, and ophthalmology students with its generated reports and predicted diagnoses during the diagnostic process. In both retrospective and prospective evaluations, OBUSight significantly outperformed residents and ophthalmology students (all p < 0.05), achieved diagnostic performance comparable to ophthalmologists, and reduced diagnostic time. In conclusion, OBUSight represents a promising AI tool for enhancing diagnostic efficiency in ophthalmic ultrasound practice, especially for less experienced clinicians.

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