A Review on Automated Facial Nerve Function Assessment From Visual Face Capture

Area of Science:

• Medical imaging
• Computer vision
• Machine learning

Background:

• Facial nerve function assessment is crucial in clinical practice.
• Current methods rely on clinician observation of facial signs.
• Automated assessment using visual face capture is an emerging field.

Purpose of the Study:

• To review studies on automated facial nerve function assessment.
• To identify challenges hindering clinical adoption.
• To suggest future research directions.

Main Methods:

• Comprehensive literature review of relevant studies.
• Analysis of image processing, computer vision, and machine learning techniques.
• Identification of common themes and challenges across studies.

Main Results:

• Numerous studies propose automated solutions for facial nerve assessment.
• No single solution has achieved widespread clinical use to date.
• Key challenges include data variability, algorithm generalizability, and clinical validation.

Conclusions:

• Automated facial nerve function assessment holds significant potential.
• Addressing identified challenges is critical for clinical translation.
• Future work should focus on robust algorithms and rigorous validation.