Assessing facial weakness in myasthenia gravis with facial recognition software and deep learning.

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<

SUBMITTER: Ruiter AM 

PROVIDER: S-EPMC10424649 | biostudies-literature | 2023 Aug

REPOSITORIES: biostudies-literature