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Objective, computerized video-based rating of blepharospasm severity.

David A Peterson1, Gwen C Littlewort2, Marian S Bartlett2

  • 1From the Computational Neurobiology Laboratory (D.A.P., T.J.S.) and Howard Hughes Medical Institute (T.J.S.), Salk Institute for Biological Studies; Institute for Neural Computation (D.A.P., G.C.L., M.S.B., T.J.S.), Kavli Institute for Brain and Mind (D.A.P., T.J.S.), Machine Perception Laboratory (G.C.L., M.S.B.), and Division of Biological Sciences (T.J.S.), University of California, San Diego, La Jolla; Sobell Department of Motor Neuroscience and Movement Disorders (A.M.), National Hospital of Neurology and Neurosurgery, Institute of Neurology, University College London, UK; Departments of Neurology, Radiology, and Anatomy and Neurobiology, and Programs in Physical Therapy and Occupational Therapy (J.S.P.), Washington University School of Medicine, St. Louis, MO; Departments of Neurology, Human Genetics, and Pediatrics (H.A.J.), Emory University, Atlanta, GA; and Human Motor Control Section (M.H.), National Institute of Neurological Disorders and Stroke, NIH, Bethesda, MD. dap@salk.edu.

Neurology
|October 23, 2016
PubMed
Summary
This summary is machine-generated.

Automated analysis of blepharospasm severity using facial expression software (CERT) showed strong correlation with traditional clinical scales. This objective method offers a simpler approach for clinical use.

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Area of Science:

  • Neurology
  • Biomedical Engineering
  • Ophthalmology

Background:

  • Blepharospasm is a focal dystonia characterized by involuntary eyelid closure.
  • Current clinical assessments rely on subjective rating scales, necessitating objective measurement tools.
  • Facial expression software offers a potential avenue for objective symptom quantification.

Purpose of the Study:

  • To compare the accuracy of automated eye closure measurements from patient videos using facial expression software against established clinical rating scales for blepharospasm severity.
  • To assess the convergent validity of the Computer Expression Recognition Toolbox (CERT) in quantifying blepharospasm symptoms.

Main Methods:

  • Video recordings of 50 blepharospasm patients were analyzed using the Computer Expression Recognition Toolbox (CERT) for eye closure duration.
  • Automated measurements were compared with scores from the Burke-Fahn-Marsden Dystonia Rating Scale, Global Dystonia Rating Scale, and Jankovic Rating Scale.
  • Spearman correlation coefficients were calculated to assess the relationship between automated measures and clinical ratings.

Main Results:

  • CERT successfully identified faces in video recordings.
  • The proportion of eye closure time measured by CERT showed significant correlations with all three clinical rating scales (Spearman ρ = 0.56, 0.52, 0.56; p < 0.0001).

Conclusions:

  • The Computer Expression Recognition Toolbox (CERT) demonstrates convergent validity for measuring blepharospasm severity.
  • Automated video analysis using CERT provides an objective and reliable method for assessing symptom severity.
  • CERT's reliance on conventional video recordings facilitates easier clinical adoption compared to EMG or kinematics.