Abstract

This study presents a novel method for the early detection of strabismus, a common eye misalignment disorder, with an emphasis on its application in telemedicine. The technique leverages synchronized eye movements to estimate the pupil location of one eye based on the other, achieving close alignment in non-strabismic cases. Regression models for each eye are developed using advanced machine learning algorithms, and significant discrepancies between estimated and actual pupil positions indicate the presence of strabismus. This approach provides a non-invasive, efficient solution for early detection and bridges the gap between basic research and clinical care by offering an accessible, machine learning-based tool that facilitates timely intervention and improved outcomes in diverse healthcare settings. The potential for pediatric screening is discussed as a possible direction for future research.