Clinical analysis of eye movement-based data in the medical diagnosis of amblyopia

Abstract

Amblyopia is an abnormal visual processing-induced developmental disorder of the central nervous system that affects static and dynamic vision, as well as binocular visual function. Currently, changes in static vision in one eye are the gold standard for amblyopia diagnosis. However, there have been few comprehensive analyses of changes in dynamic vision, especially eye movement, among children with amblyopia. Here, we proposed an optimization scheme involving a video eye tracker combined with an "artificial eye" for comprehensive examination of eye movement in children with amblyopia; we sought to improve the diagnostic criteria for amblyopia and provide theoretical support for practical treatment. The resulting eye movement data were used to construct a deep learning approach for diagnostic and predictive applications. Through efforts to manage the uncooperativeness of children with strabismus who could not complete the eye movement assessment, this study quantitatively and objectively assessed the clinical implications of eye movement characteristics in children with amblyopia. Our results indicated that an amblyopic eye is always in a state of adjustment, and thus is not "lazy." Additionally, we found that the eye movement parameters of amblyopic eyes and eyes with normal vision are significantly different. Finally, we identified eye movement parameters that can be used to supplement and optimize the diagnostic criteria for amblyopia, providing a diagnostic basis for evaluation of binocular visual function.