ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.
basic_science · Level V
Where this comes from
- Record sourced from PubMed, PMID 41266592.
- Also identified by DOI 10.1038/s41746-025-02062-7 and PMC identifier 12635282.
- Licence recorded as CC BY-NC-ND.
- Because redistribution is not established, this page shows the abstract only. Follow the links below for the full text.
Abstract
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.