Accepted for/Published in: JMIR Dermatology
Date Submitted: Dec 8, 2021
Date Accepted: Apr 16, 2022
Date Submitted to PubMed: Oct 30, 2024
Current Landscape of Generative Adversarial Network for Facial De-Identification in Dermatology: A Systematic Review and Evaluation
ABSTRACT
Background:
De-identifying facial images is critical for protecting patient anonymity in the era of increasing tools for automatic image analysis in dermatology.
Objective:
The purpose of this paper was to review the current literature in the field of automatic facial de-identification algorithms.
Methods:
We conducted a systematic search using a combination of headings and keywords to encompass the concepts of facial de-identification and privacy preservation. The databases MEDLINE (via Pubmed), Embase (via Elsevier) and Web of Science (via Clarivate) were queried from inception to 5/1/2021. Studies of wrong design and outcomes were excluded during the screening and review process.
Results:
A total of 18 studies were included in the final review reporting various methodologies of facial de-identification algorithms. The study methods were rated individually for their utility for use cases in dermatology pertaining to skin color/pigmentation and texture preservation, data utility, and human detection. Most studies notable in the literature address feature preservation while sacrificing skin color and texture.
Conclusions:
Facial de-identification algorithms are sparse and inadequate to preserve both facial features and skin pigmentation/texture quality in facial photographs. A novel approach is needed to ensure greater patient anonymity, while increasing data access for automated image analysis in dermatology for improved patient care.
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Copyright
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