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<p>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, <i>κ</i> &gt; 0.90). It achieves 100% diagnostic sensitivity and high agreement (<i>κ</i> &gt; 0.90) across eleven eye diseases in three cohorts, anonymizing over 95% of images. ROFI works with AI systems, maintaining original diagnoses (<i>κ</i> &gt; 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.</p>

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ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年11月20日

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  • Yuan Tian,
  • Min Zhou,
  • Yitong Chen,
  • Fang Li,
  • Lingzi Qi,
  • Shuo Wang,
  • Xieyang Xu,
  • Yu Yu,
  • Shiqiong Xu,
  • Chaoyu Lei,
  • Yankai Jiang,
  • Rongzhao Zhang,
  • Jia Tan,
  • Li Wu,
  • Hong Chen,
  • Xiaowei Liu,
  • Wei Lu,
  • Lin Li,
  • Huifang Zhou,
  • Xuefei Song,
  • Guangtao Zhai,
  • Xianqun Fan

摘要

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.