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Updated: May 29, 2025

Single-stage Dynamic Reanimation of the Smile in Irreversible Facial Paralysis by Free Functional Muscle Transfer
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Dynamic blinking feature extraction for automated facial nerve paralysis detection.

Akara Supratak1, Watsaporn Pornwatanacharoen2, Varit Rungbanapan1

  • 1Faculty of Information and Communication Technology, Mahidol University, 999 Phuttamonthon 4 Road, Nakhon Pathom, 73170, Thailand.

Computers in Biology and Medicine
|February 6, 2025
PubMed
Summary

Facial nerve paralysis (FNP) can be detected using a new automated system that analyzes dynamic blink features from videos. This method significantly improves FNP detection accuracy compared to traditional static assessments.

Keywords:
BlinkingDynamic blink parametersFacial nerve paralysisHigh-frame-rate videoMachine learning

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Area of Science:

  • Ophthalmology
  • Biomedical Engineering
  • Machine Learning

Background:

  • Facial nerve paralysis (FNP) impairs eyelid function, increasing risks of ophthalmic complications and vision loss.
  • Current FNP detection relies on static facial analysis, neglecting dynamic blink characteristics crucial for treatment assessment.

Purpose of the Study:

  • To develop and validate an automated system for extracting dynamic blink features from high-frame-rate videos for objective FNP detection.
  • To compare the efficacy of dynamic blink features against static parameters in diagnosing FNP.

Main Methods:

  • Developed algorithms for dynamic blink feature extraction using facial landmark detection from high-frame-rate videos.
  • Utilized an Isolation Forest model to generate normality scores for blink pairs, indicating upper eyelid movement abnormality.
  • Evaluated the system on 103 subjects (86 healthy, 17 with FNP).

Main Results:

  • The machine learning model using normality scores achieved a 75% higher F1-score than static parameters and a 35% higher F1-score than other dynamic parameters for FNP detection.
  • The normality score of closing blink velocity emerged as the most significant feature for distinguishing FNP.
  • The system demonstrated superior performance in objectively assessing blink dynamics.

Conclusions:

  • Dynamic blink features offer a promising objective measure for FNP detection, complementing traditional facial asymmetry assessments.
  • The developed automated system shows potential for improved diagnosis and monitoring of FNP.
  • Further research is warranted to explore the full clinical utility of these dynamic blink metrics.