Fine-Tuning on AI-Driven Video Analysis through Machine Learning: Development of an Automated Evaluation Tool of Facial Palsy.

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).

Methods: A total of 1181 images extracted from the videos of 196 patients were enrolled in the training, and these images underwent manual correction of 68 keypoints. The annotated data were integrated into the previous model with a stack of 2 hourglass networks combined with channel aggregation block.

Results: The postannotation model showed improvement in normalized mean error from 0.026 to 0.018, and qualitative keypoint detection on each facial unit revealed improvements.

Conclusions: Strict control of inter- and intra-annotator variability successfully fine-tuned the presented model. The new model is a promising solution for objective assessment of facial palsy.