Yuan Tian, Min Zhou, Yitong Chen, Fang Li, Lingzi Qi, Shuo Wang, Xiaojun Xu, Yu Yu, Shiqiong Xu, Chaoyu Lei, Yankai Jiang, Rongzhao Zhang, Jia Tan, Wu Li, Hong Chen, Xiaowei Liu, Wei Lü, Lin Li, Huifang Zhou, Xuefei Song, Guangtao Zhai, Xianqun Fan
Patient face images provide a convenient mean for evaluating eye diseases, while also raising privacy concerns. Here, we introduce ROFI, a deep learning-based privacy protection framework for ophthalmology. Using weakly supervised learning and neural identity translation, ROFI anonymizes facial features while retaining disease features (over 98% accuracy, κ > 0.90). It achieves 100% diagnostic sensitivity and high agreement (κ > 0.90) across eleven eye diseases in three cohorts, anonymizing over 95% of images. ROFI works with AI systems, maintaining original diagnoses (κ > 0.80), and supports secure image reversal (over 98% similarity), enabling audits and long-term care. These results show ROFI's effectiveness of protecting patient privacy in the digital medicine era.