Artificial Intelligence for the Analysis of Video Data of Facial Movement, with a Focus on Myasthenia Gravis

Rationale: Myasthenia Gravis (MG) is an autoimmune disorder (AID) with antibodies against the NMJ, resulting in various degrees of muscle fatigability and weakness. All striated muscles can be involved, although the extra-ocular muscles are most commonly affected, giving rise to a fluctuating ptosis and diplopia. Facial muscles are also commonly affected, resulting in eye closure weakness, difficulty chewing and swallowing or speech impairments. Antibodies against the acetylcholine receptor (AChR) are present in over 80% of generalized MG patients. In the pure ocular form, AChR antibodies are detectable in nearly 50% of all patients. In approximately 4%, antibodies against the postsynaptic muscle-specific receptor tyrosine kinase (MuSK) are found and in 15% of the patients with generalized disease, no serum antibodies are detected1-3. Approximately 15% of AChR MG patients has a thymoma, in which case the disease can be classified as a paraneoplastic syndrome2. With a prevalence of 1 to 2 per 10.000, MG is considered a rare disease2. The rarity of MG can make it difficult to diagnose, specifically for general Neurologists who are likely to encounter a patient with MG only a handful of times throughout their career. In addition, the fluctuating nature of the disease makes it difficult to make appropriate treatment decisions, especially as patients throughout the country are usually treated at one specialized center (in the Netherlands, the LUMC). Currently, patients who are in doubt whether they are experiencing an exacerbation have to make an appointment and travel for several hours to undergo assessment by their specialized Neurologist. An objective, reliable biomarker for disease severity that can be used at home would therefore greatly improve quality of life for many MG patients. Emerging possibilities in modern technologies can support doctors with all kinds of medical challenges, like offering diagnostic support, treatment decisions or patient follow-up. A technology of special interest for this study is advanced facial recognition. We aim to study the ability of existing software (FaceReader, Noldus) versus a deep learning model specifically developed for this purpose by the group of Jan van Gemert at the TU Delft to differentiate between healthy controls and patients with MG and between MG patients with different levels of disease severity. Primary objectives: To determine and compare the diagnostic yield of two different methods (FaceReader technology and a deep learning model specifically developed for video data) to analyse facial weakness from video recordings (04:00m) with different standardized facial expressions to: 1. Differentiate between MG patients and healthy controls. 2. Differentiate between mild and moderate to severe disease severity.

Sex

All

Minimum age

18 Years

Inclusion Criteria:

* Male or female participants aged ≥ 18 years
* Subjects must understand the requirements of the study and provide written informed consent.

MG

* Clinical signs or symptoms suggestive of MG and at least one of the following:
* A serologic test for AChR antibodies or MuSK antibodies or
* A diagnostic electrophysiological investigation supportive of the diagnosis MG or
* A positive neostigmine test Healthy control group
* Volunteers from spouses, friends and family accompanying patient or employees from our department
* No medical conditions affecting the facial muscles, e.g. Graves' disease, previous stroke with a facial palsy
* No use of medication affecting the facial features, e.g. prednisone

Exclusion Criteria:

* Inability to give written informed consent
* Inability to read Dutch/ English video-instructions
* Participants with active Graves' disease or unilateral facial paralysis