Novel Baseline Facial Muscle Database Using Statistical Shape Modeling and In Silico Trials toward Decision Support...

Area of Science:

• Biomedical Engineering
• Computational Anatomy

Background:

• Facial palsy significantly impacts patients' lives, necessitating effective rehabilitation strategies.
• Current computer-aided systems lack essential facial muscle baseline data for optimal patient assessment and treatment guidance.

Purpose of the Study:

• To develop a novel facial muscle database incorporating static and dynamic behaviors.
• To utilize statistical shape modeling (SSM) and in-silico trials for creating this database.

Main Methods:

• Generated 10,000 virtual subjects using SSM head models.
• Defined skull and muscle networks, computed muscle strains for smiling and kissing mimics.
• Validated predictions against CT-based models and literature data.

Main Results:

• Achieved median deviations of 2.1-2.2 mm for skull predictions and 4.89 mm for muscle lengths.
• Computed muscle strains aligned with previous studies and literature.
• Demonstrated the feasibility of in-silico methods for facial muscle analysis.

Conclusions:

• The novel facial muscle database facilitates accurate evaluation of facial palsy patients' muscle states.
• Enables optimal selection of rehabilitation exercises tailored to individual patient needs.
• Future integration into clinical decision support systems will automate malfunction detection and personalized rehabilitation game recommendations.