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

Authors

Quentin Lesport, Department of Surgery, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
Guillaume Joerger, Care Constitution Corp., Newark, DE 19702, USA.
Henry J. Kaminski, Department of Neurology & Rehabilitation Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
Helen Girma, Department of Neurology & Rehabilitation Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
Sienna McNett, Department of Neurology & Rehabilitation Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
Mohammad Abu-Rub, Department of Neurology & Rehabilitation Medicine, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.
Marc Garbey, Department of Surgery, School of Medicine and Health Sciences, George Washington University, Washington, DC 20037, USA.

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.

APA Citation

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.
https://hsrc.himmelfarb.gwu.edu/gwhpubs/3435

Department

Surgery