Enhancing pathological myopia diagnosis: a bimodal artificial intelligence approach integrating fundus and optical coherence tomography imaging for precise atrophy, traction and neovascularisation grading

作者：Zhiyan Xu, Yajie Yang, Huan Chen, Ruoan Han, Xiaoxu Han, Jianchun Zhao, Weihong Yu, Zhikun Yang, Youxin Chen · 发表于：British Journal of Ophthalmology · 年份：2025 · DOI：10.1136/bjo-2024-326252 · 被引用次数：3 · 研究领域：Retinal Imaging and Analysis、Retinal Diseases and Treatments、Ophthalmology and Visual Impairment Studies

BACKGROUND: Pathological myopia (PM) has emerged as a leading cause of global visual impairment, early detection and precise grading of PM are crucial for timely intervention. The atrophy, traction and neovascularisation (ATN) system is applied to define PM progression and stages with precision. This study focuses on constructing a comprehensive PM image dataset comprising both fundus and optical coherence tomography (OCT) images and developing a bimodal artificial intelligence (AI) classification model for ATN grading in PM. METHODS: This single-centre retrospective cross-sectional study collected 2760 colour fundus photographs and matching OCT images of PM from January 2019 to November 2022 at Peking Union Medical College Hospital. Ophthalmology specialists labelled and inspected all paired images using the ATN grading system. The AI model used a ResNet-50 backbone and a multimodal multi-instance learning module to enhance interaction across instances from both modalities. RESULTS: Performance comparisons among single-modality fundus, OCT and bimodal AI models were conducted for ATN grading in PM. The bimodality model, dual-deep learning (DL), demonstrated superior accuracy in both detailed multiclassification and biclassification of PM, which aligns well with our observation from instance attention-weight activation maps. The area under the curve for severe PM using dual-DL was 0.9635 (95% CI 0.9380 to 0.9890), compared with 0.9359 (95% CI 0.9027 to 0.9691) for the solely ...