Fine-Tuning on AI-Driven Video Analysis through Machine Learning: Development of an Automated Evaluation Tool of Facial Palsy.
Journal:
Plastic and reconstructive surgery
Published Date:
Dec 17, 2024
Abstract
BACKGROUND: Establishment of a quantitative, objective evaluation tool for facial palsy has been a challenging issue for clinicians and researchers, and artificial intelligence-driven video analysis can be considered a reasonable solution. The authors introduced facial keypoint detection, which detects facial landmarks with 68 points, but existing models had been organized almost solely with images of healthy individuals, and low accuracy was presumed in the prediction of asymmetric faces of patients with facial palsy. The accuracy of the existing model was assessed by applying it to videos of 30 patients with facial palsy. Qualitative review clearly showed its insufficiency. The model was prone to detect patients' faces as symmetric, and was unable to detect eye closure. Thus, the authors enhanced the model through the machine-learning process of annotation (ie, fine-tuning).