Myasthenia Gravis (MG) is an autoimmune neuromuscular disease. Given that extraocular muscle manifestations are the initial and primary symptoms in most patients, ocular muscle assessment is regarded necessary early screening tool. To overcome the limitations of the manual clinical method, an intuitive idea is to collect data via imaging devices, followed by analysis or processing using Deep Learning (DL) techniques (particularly image segmentation approaches) to enable automatic MG evaluation. Unfortunately, their clinical applications in this field have not been thoroughly explored. To bridge this gap, our study prospectively establishes a new DL-based system to promote the diagnosis of MG disease, with a complete workflow including facial data acquisition, eye region localization and ocular structure segmentation. Experimental results demonstrate that the proposed system achieves superior segmentation performance of ocular structure. Moreover, it markedly improves the diagnostic accuracy of doctors. In the future, this endeavor can offer highly promising MG monitoring tools for healthcare professionals, patients, and regions with limited medical resources.

The current mode of clinical aided diagnosis of Ocular Myasthenia Gravis (OMG) is time-consuming and laborious, and it lacks quantitative standards. An aided diagnostic system for OMG is proposed to solve this problem. The values calculated by the system include three clinical indicators: eyelid distance, sclera distance, and palpebra superior fatigability test time. For the first two indicators, the semantic segmentation method was used to extract the pathological features of the patient’s eye image and a semantic segmentation model was constructed. The patient eye image was divided into three regions: iris, sclera, and background. The indicators were calculated based on the position of the pixels in the segmentation mask. For the last indicator, a calculation method based on the Eyelid Aspect Ratio (EAR) is proposed; this method can better reflect the change of eyelid distance over time. The system was evaluated based on the collected patient data. The results show that the segmentation model achieves a mean Intersection-Over-Union (mIoU) value of 86.05%. The paired-sample T-test was used to compare the results obtained by the system and doctors, and the $p$ values were all greater than 0.05. Thus, the system can reduce the cost of clinical diagnosis and has high application value.