ROFI: A Deep Learning-Based Ophthalmic Sign-Preserving and Reversible Patient Face Anonymizer
This addresses privacy concerns for patients in digital medicine by enabling secure use of facial images for eye disease evaluation, though it is incremental as it builds on existing anonymization and neural translation methods.
The paper tackled the problem of protecting patient privacy in ophthalmology by developing ROFI, a deep learning framework that anonymizes facial features while preserving disease features, achieving over 98% accuracy and high diagnostic sensitivity across multiple eye diseases.
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.