The face of neuromuscular dysfunction: artificial intelligence for the analysis of video data of facial movement, with a focus on Myasthenia Gravis

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 detected.
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 disease.
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.

observational prospective case-control study.

patients and healthy volunteers will be asked to participate in a one-time
video recording of 04:00 minute, a subgroup of MG will be asked to undergo
multiple videos over time. There are no risks involved in participating.
MG patients who do not undergo a QMG for their standard care will be asked to
undergo a QMG test. This is a clinical test for establishing disease severity
and is widely used in clinical practice. Performing a QMG will take 5 minutes
and consists of an assessment of muscle fatigability. There are no risks
involved, except for a minor risk of discomfort when patients with difficulties
swallowing are asked to drink half a cup of water. This risk will be minimized
by leaving out this item when known difficulties with swallowing are present,
as is common clinical practice. The benefit of participation is the possible
future development of a diagnostic tool for physicians and the possibility of
improved clinical care through automated remote monitoring of disease severity
through video recording.
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