Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination

Document Type

Journal Article

Publication Date

9-7-2023

Journal

Sensors (Basel, Switzerland)

Volume

23

Issue

18

DOI

10.3390/s23187744

Keywords

computer vision; deep learning; diplopia; eyes tracking; myasthenia gravis; neurological disease; ptosis; telehealth; telemedicine

Abstract

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.

Department

Surgery

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