ArticleMayo Clinic proceedings. Digital health2023
Artificial Intelligence-Based Face Transformation in Patient Seizure Videos for Privacy Protection.
Article in Mayo Clinic proceedings. Digital health, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.
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Who cites it
3 citing papers in PubMed.
- Reply to: Artificial Intelligence Face Swapping: Promise and Peril in Health Care.Mayo Clinic proceedings. Digital health · 2024Article
- Untapped Potential of Artificial Intelligence for Analysis of Epileptic Seizure Videos: A Clinician's Expectation.Mayo Clinic proceedings. Digital health · 2024Article
- Artificial Intelligence Face Swapping: Promise and Peril in Health Care.Mayo Clinic proceedings. Digital health · 2024Article
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Authors and funding
10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: To investigate the feasibility and accuracy of artificial intelligence (AI) methods of facial deidentification in hospital-recorded epileptic seizure videos, for improved patient privacy protection while preserving clinically important features of seizure semiology. Patients and Methods: Videos of epileptic seizures displaying seizure-related involuntary facial changes were selected from recordings at Taipei Veterans General Hospital Epilepsy Unit (between August 1, 2020 and February 28, 2023), and a single representative video frame was prepared per seizure. We tested 3 AI transformation models: (1) morphing the original facial image with a different male face; (2) substitution with a female face; and (3) cartoonization. Facial deidentification and preservation of clinically relevant facial detail were calculated based on: (1) scoring by 5 independent expert clinicians and (2) objective computation. Results: According to the clinician scoring of 26 facial frames in 16 patients, the best compromise between deidentification and preservation of facial semiology was the cartoonization model. A male facial morphing model was superior to the cartoonization model for deidentification, but clinical detail was sacrificed. Objective similarity testing of video data reported deidentification scores in agreement with the clinicians' scores; however, preservation of semiology gave mixed results likely due to inadequate existing comparative databases. Conclusion: Artificial intelligence-based face transformation of medical seizure videos is feasible and may be useful for patient privacy protection. In our study, the cartoonization approach provided the best compromise between deidentification and preservation of seizure semiology.
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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.