ArticleNPJ digital medicine2025
ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.
Article in NPJ digital medicine, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
4 citing papers in PubMed.
- Benchmarking clinical knowledge and multi-modal reasoning of large language models in liver cirrhosis.Scientific reports · 2026Article
- An adaptive differential privacy framework for clinical llms with context-aware noise calibration, hierarchical budgeting, and real-time auditing.Scientific reports · 2026Article
- ROFI: a deep learning-based ophthalmic sign-preserving and reversible patient face anonymizer.Annals of medicine and surgery (2012) · 2026Article
- Analyzing rescaling, discretization, and linearization in RNNs for neural system modeling.Frontiers in computational neuroscience · 2026Article
Corrections and comments
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Authors and funding
22 authors.
Funding
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.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.