Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination
Sensors (Basel, Switzerland)
computer vision; deep learning; diplopia; eyes tracking; myasthenia gravis; neurological disease; ptosis; telehealth; telemedicine
Due to the precautions put in place during the COVID-19 pandemic, utilization of telemedicine has increased quickly for patient care and clinical trials. Unfortunately, teleconsultation is closer to a video conference than a medical consultation, with the current solutions setting the patient and doctor into an evaluation that relies entirely on a two-dimensional view of each other. We are developing a patented telehealth platform that assists with diagnostic testing of ocular manifestations of myasthenia gravis. We present a hybrid algorithm combining deep learning with computer vision to give quantitative metrics of ptosis and ocular muscle fatigue leading to eyelid droop and diplopia. The method works both on a fixed image and frame by frame of the video in real-time, allowing capture of dynamic muscular weakness during the examination. We then use signal processing and filtering to derive robust metrics of ptosis and l ocular misalignment. In our construction, we have prioritized the robustness of the method versus accuracy obtained in controlled conditions in order to provide a method that can operate in standard telehealth conditions. The approach is general and can be applied to many disorders of ocular motility and ptosis.
Lesport, Quentin; Joerger, Guillaume; Kaminski, Henry J.; Girma, Helen; McNett, Sienna; Abu-Rub, Mohammad; and Garbey, Marc, "Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination" (2023). GW Authored Works. Paper 3435.