Ptosis in myasthenia gravis: extended fatigue and recovery bedside test

Development and Assessment of an Artificial Intelligence-Based Tool for Ptosis Measurement in Adult Myasthenia Gravis Patients Using Selfie Video Clips Recorded on Smartphones

Introduction

Myasthenia gravis (MG) is a rare autoimmune disease characterized by muscle weakness and fatigue. Ptosis (eyelid drooping) occurs due to fatigue of the muscles for eyelid elevation and is one symptom widely used by patients and healthcare providers to track progression of the disease. Margin reflex distance 1 (MRD1) is an accepted clinical measure of ptosis and is typically assessed using a hand-held ruler. In this work, we develop an AI model that enables automated measurement of MRD1 in self-recorded video clips collected using patient smartphones.

Methods

A 3-month prospective observational study collected a dataset of video clips from patients with MG. Study participants were asked to perform an eyelid fatigability exercise to elicit ptosis while filming "selfie" videos on their smartphones. These images were collected in nonclinical settings, with no in-person training. The dataset was annotated by non-clinicians for (1) eye landmarks to establish ground truth MRD1 and (2) the quality of the video frames. The ground truth MRD1 (in millimeters, mm) was calculated from eye landmark annotations in the video frames using a standard conversion factor, the horizontal visible iris diameter of the human eye. To develop the model, we trained a neural network for eye landmark detection consisting of a ResNet50 backbone plus two dense layers of 78 dimensions on publicly available datasets. Only the ResNet50 backbone was used, discarding the last two layers. The embeddings from the ResNet50 were used as features for a support vector regressor (SVR) using a linear kernel, for regression to MRD1, in mm. The SVR was trained on data collected remotely from MG patients in the prospective study, split into training and development folds. The model's performance for MRD1 estimation was evaluated on a separate test fold from the study dataset.

Results

On the full test fold (N = 664 images), the correlation between the ground truth and predicted MRD1 values was strong (r = 0.732). The mean absolute error was 0.822 mm; the mean of differences was -0.256 mm; and 95% limits of agreement (LOA) were -0.214-1.768 mm. Model performance showed no improvement when test data were gated to exclude "poor" quality images.

Conclusions

On data generated under highly challenging real-world conditions from a variety of different smartphone devices, the model predicts MRD1 with a strong correlation (r = 0.732) between ground truth and predicted MRD1.

Bedside and laboratory diagnostic testing in myasthenia

Myasthenia gravis (MG) and congenital myasthenic syndromes (CMS) are a group of disorders with a well characterised autoimmune or genetic and neurophysiological basis. We reviewed the literature from the last 20 years assessing the utility of various neurophysiological, immunological, provocative and genetic tests in MG and CMS. Diagnostic sensitivity of repetitive nerve stimulation test ranges between 14 and 94% and specificity between 73 and 100%; sensitivity of single-fibre EMG (SFEMG) test ranges between 64 and 100% and specificity between 22 and 100%; anti-acetylcholine receptor (AChR) antibody sensitivity ranges from 13 to 97% and specificity ranges from 95 to 100%. Overall, SFEMG has the highest sensitivity while positive anti-AChR antibodies have the highest specificity. Newer testing strategies that have been investigated over the last couple of decades include ocular vestibular-evoked myogenic potentials, otoacoustic emissions and disease-specific circulating miRNAs in serum for autoimmune myasthenia, as well as next-generation sequencing for genetic testing of CMS. While there has been significant progress in developing newer testing strategies for diagnosing MG and CMS over the last couple of decades, more research is needed to assess the utility of these newer tools regarding their sensitivity and specificity.

On the frontline: what an optometrist needs to know about myasthenia gravis

BACKGROUND

Myasthenia gravis (MG) is an autoimmune disease that affects the voluntary skeletal muscles. It is characterized by transient weakness of the muscles that improves with rest. Muscle weakness involving the eyes can produce signs or symptoms of diplopia, blurred vision, ptosis, and ophthalmoplegia. Ptosis is defined as an abnormal eyelid "drooping" beyond the normal 1 to 2 mm of the upper limbus of the cornea. Hence, most patients with MG have ophthalmic manifestations. Among all patients with MG, up to half will have exclusively ocular symptoms. In these cases, the condition is referred to as ocular myasthenia.

CASE REPORT

A 60-year-old man was referred from a neurology clinic for management of intermittent diplopia for greater than 1 year and intermittent bilateral ptosis for the prior year. He reported that he first noticed symptoms of MG at the age of 42, but did not receive the diagnosis until 1 year before his aforementioned neurology examination. He was prescribed spectacles with bilateral ptosis crutches. A diagnosis of severe seronegative MG was subsequently confirmed with neurologic examination and antibody testing.

CONCLUSIONS

Because patients with undiagnosed myasthenia gravis may present initially with ocular signs or symptoms, it is important for the optometrist to be familiar with the condition and the simple "in-office" tests that can be performed to establish a tentative diagnosis and management plan. The optometrist can also participate in the management of ocular manifestations of myasthenia and should be familiar with the use of a ptosis crutch (in addition to prism spectacles or occlusion therapies if indicated) as a nonsurgical intervention for ptosis.