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A Protocol for Comprehensive Assessment of Bulbar Dysfunction in Amyotrophic Lateral Sclerosis ALS
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Video Assessment to Detect Amyotrophic Lateral Sclerosis.

Guilherme Camargo Oliveira1,2, Quoc Cuong Ngo2, Leandro Aparecido Passos1

  • 1School of Science, São Paulo State University, São Paulo, Brazil.

Digital Biomarkers
|October 30, 2024
PubMed
Summary

Computerized analysis of facial expression videos can detect amyotrophic lateral sclerosis (ALS). This method achieved 91% accuracy in identifying ALS patients by analyzing specific facial muscle movements.

Keywords:
Amyotrophic lateral sclerosisFacial action unitsFacial expressionLogistic regressionMachine learning

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Area of Science:

  • Neurology
  • Computer Science
  • Biomedical Engineering

Background:

  • Facial muscle weakness is an early symptom of amyotrophic lateral sclerosis (ALS).
  • Current diagnostic methods relying on visual assessment of facial expressions can be subjective due to individual variations.
  • Objective, quantitative methods are needed for early and accurate ALS detection.

Purpose of the Study:

  • To investigate the efficacy of computerized facial expression analysis in differentiating ALS patients from healthy individuals.
  • To identify specific facial action units and expressions most indicative of ALS-related facial weakness.

Main Methods:

  • Utilized the Toronto NeuroFace Dataset containing videos of nine facial expression tasks from ALS patients and healthy controls.
  • Analyzed facial action units extracted from these videos to build classification models.
  • Evaluated the performance of the models in distinguishing between the two groups.

Main Results:

  • The study achieved a peak classification accuracy of 0.91.
  • The 'pretending to smile with tight lips' facial expression yielded the highest accuracy.
  • Specific action units related to lip and mouth movements were key indicators.

Conclusions:

  • Computerized analysis of facial expressions shows significant potential for objective ALS detection.
  • This approach can help overcome the subjectivity inherent in traditional diagnostic methods.
  • Further research can refine this technique for clinical application in identifying facial weakness in ALS.