Deep Learning Detection of Subtle Torsional Eye Movements: Preliminary Results

Abstract

The control of torsional eye position is a key component of ocular motor function. Ocular torsion can be affected by pathologies that involve ocular motor pathways, spanning from the vestibular labyrinth of the inner ears to various regions of the brainstem and cerebellum. Timely and accurate diagnosis enables efficient interventions and management of each case which are crucial for patients with dizziness, vertical double vision, or imbalance. Such detailed evaluation of eye movements may not be possible in all frontline clinical settings, particularly for detecting torsional abnormalities. These abnormalities are often more challenging to identify at the bedside compared to horizontal or vertical eye movements. To address these challenges, we used a dataset of induced torsional eye movements recorded with video-oculography (VOG) to develop video-based deep learning models for detecting ocular torsion. Our models achieve 0.9308 AUROC and 86.79% accuracy, using ocular features and neurophysiology-supported phenomena.