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De-identification Strategy and Re-identification Risks for Facial Computed Tomography Images via Deep Learning
Seong Uk Kang1,2, Ickjun Kim3, Sang Won Park4
1Department of Medical Information, Kangwon National University Hospital, Chuncheon, Republic of Korea.
Journal of Imaging Informatics in Medicine
|February 25, 2026
Summary
A new deep learning method selectively removes soft tissue from head CT scans, protecting patient privacy while preserving bone structures. This significantly reduces re-identification risk without harming data utility for facial CT research.
Area of Science:
- Medical Imaging
- Artificial Intelligence
- Computer Vision
Background:
- Head computed tomography (CT) scans contain sensitive facial information, posing re-identification risks.
- Existing de-identification methods may compromise facial bone structure integrity.
- Effective privacy protection is crucial for sharing medical imaging data.
Purpose of the Study:
- To develop and evaluate a deep learning-based selective de-identification method for head CT images.
- To remove facial soft-tissue features while preserving facial bone structures.
- To assess re-identification risk after de-identification for robust privacy protection.
Main Methods:
- A retrospective study included 3206 facial CT scans from 3091 patients.
- A YOLOv8-based model was used to selectively remove facial soft-tissue features.
- Re-identification risk was assessed using deep learning facial embeddings and human evaluation.
Main Results:
- The de-identification model achieved a mean average precision (mAP) of 0.858.
- Deep learning-based re-identification accuracy decreased from 85% to 64%.
- Human re-identification accuracy dropped from 84% to 55% for both general participants and plastic surgeons.
Conclusions:
- A selective de-identification method for facial CT images was successfully developed.
- The method effectively preserves craniofacial structures and significantly reduces re-identification risk.
- The publicly available model and demo facilitate broader adoption in facial CT research.
