Pan-retinal pathology detection in oct scans integrating natural language synthesis with diagnostic annotation

Abstract

Abstract While OCT is pivotal for macular disease diagnosis, its adoption in primary care is limited by AI systems that cannot simultaneously analyze multi-sectional scans across the full spectrum of maculopathies or generate diagnostically integrated reports. Here we present iOCT, an intelligent OCT analysis system that integrates a multi-level annotation framework with data distillation to enable automated multi-sectional scan analysis and comprehensive natural language report generation. iOCT was trained on 107,790 macular OCT scans (1,296,439 images) and annotated across four levels combining case-level natural language descriptions with image/study-level diagnostic classifications. Internally, iOCT achieved BLEU-1 of 0.5995 for report generation—outperforming all baselines including R2Gen—and a mean AUC of 0.988 (95% CI: 0.985–0.991) for diagnostic classification. In prospective multicenter validation across ten hospitals (8998 cases), Integrated Reports achieved physician-level quality in 96.4% of cases (mean 2.94/3), significantly outperforming NL Reports (79.5%, 2.60/3), with the greatest gains at previously low-performing centers (1.38–2.93). iOCT matched junior ophthalmologists in speed (23.37 s vs. 24.44 s, p = 0.368) and report quality (2.91 vs. 2.88, p = 0.279), outperformed residents ( p < 0.001), and approached senior specialists (19.47 s, 2.98). These findings establish iOCT as a deployable, multi-disease OCT system with performance approaching trained ophthalmologists, supporting large-scale retinal disease screening in primary care.