Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning
Annabel M. Ruiter (Leiden University Medical Center)
Ziqi Wang (TU Delft - Pattern Recognition and Bioinformatics)
Zhao Yin (Student TU Delft)
Willemijn C. Naber (Leiden University Medical Center)
Jerrel Simons (Leiden University Medical Center)
Jurre T. Blom (Jurreblom, Apeldoorn)
Jan C. van Gemert (TU Delft - Pattern Recognition and Bioinformatics)
Jan J.G.M. Verschuuren (Leiden University Medical Center)
Martijn R. Tannemaat (Leiden University Medical Center)
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Abstract
Objective: Myasthenia gravis (MG) is an autoimmune disease leading to fatigable muscle weakness. Extra-ocular and bulbar muscles are most commonly affected. We aimed to investigate whether facial weakness can be quantified automatically and used for diagnosis and disease monitoring. Methods: In this cross-sectional study, we analyzed video recordings of 70 MG patients and 69 healthy controls (HC) with two different methods. Facial weakness was first quantified with facial expression recognition software. Subsequently, a deep learning (DL) computer model was trained for the classification of diagnosis and disease severity using multiple cross-validations on videos of 50 patients and 50 controls. Results were validated using unseen videos of 20 MG patients and 19 HC. Results: Expression of anger (p = 0.026), fear (p = 0.003), and happiness (p < 0.001) was significantly decreased in MG compared to HC. Specific patterns of decreased facial movement were detectable in each emotion. Results of the DL model for diagnosis were as follows: area under the curve (AUC) of the receiver operator curve 0.75 (95% CI 0.65–0.85), sensitivity 0.76, specificity 0.76, and accuracy 76%. For disease severity: AUC 0.75 (95% CI 0.60–0.90), sensitivity 0.93, specificity 0.63, and accuracy 80%. Results of validation, diagnosis: AUC 0.82 (95% CI: 0.67–0.97), sensitivity 1.0, specificity 0.74, and accuracy 87%. For disease severity: AUC 0.88 (95% CI: 0.67–1.0), sensitivity 1.0, specificity 0.86, and accuracy 94%. Interpretation: Patterns of facial weakness can be detected with facial recognition software. Second, this study delivers a ‘proof of concept’ for a DL model that can distinguish MG from HC and classifies disease severity.
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