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Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer

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Quantitative Assessment of Facial Paralysis Using Dynamic 3D Photogrammetry and Deep Learning: A Hybrid Approach

Xiangyang Ju¹, Ashraf Ayoub², Stephen Morley³

¹Medical Devices Unit, Department of Clinical Physics and Bioengineering, NHS Greater Glasgow and Clyde, Glasgow G3 8SJ, UK.

Sensors (Basel, Switzerland)

|September 19, 2025

Summary

This summary is machine-generated.

This study introduces a deep learning method using 3D facial movement data and expert input to objectively assess facial paralysis severity. The novel approach achieved over 95% accuracy, improving diagnostic reliability.

Keywords:

PointNet dynamic 3D photogrammetry facial paralysis machine learning

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Area of Science:

Medical imaging
Artificial intelligence
Biomedical engineering

Background:

Subjective clinical assessment of facial paralysis lacks reproducibility.
Intra-observer and inter-observer variability limit current diagnostic methods.
Objective quantification is needed for accurate facial paralysis assessment.

Purpose of the Study:

To develop and validate a deep learning model for objective facial paralysis severity quantification.
To combine 3D facial movement data with expert consensus for improved diagnostic accuracy.
To overcome the limitations of subjective clinical evaluations.

Main Methods:

Utilized a dynamic 3D photogrammetry system to capture facial movements during five expressions.
Extracted point clouds representing facial geometry at rest and maximum expression.
Trained a PointNet deep learning model integrating point clouds and expert grading.

Main Results:

The deep learning model achieved over 95% accuracy in assessing facial paralysis severity.
Demonstrated the feasibility of combining 3D imaging and AI for objective clinical assessment.
Quantified facial paralysis with high precision, exceeding subjective grading reliability.

Conclusions:

Deep learning combined with 3D photogrammetry offers a robust method for objective facial paralysis assessment.
This approach enhances diagnostic accuracy and overcomes inter-observer variability.
Future applications may include remote patient monitoring and personalized treatment planning.