VF-HM : vision loss estimation using fundus photograph for high myopia

Abstract

High myopia (HM) is a leading cause of irreversible vision loss due to its association with various ocular complications including myopic maculopathy (MM). Visual field (VF) sensitivity systematically quantifies visual function, thereby revealing vision loss, and is integral to the evaluation of HM-related complications. However, measuring VF is subjective and time-consuming as it highly relies on patient compliance. Conversely, fundus photographs provide an objective measurement of retinal morphology, which reflects visual function. Therefore, utilizing machine learning models to estimate VF from fundus photographs becomes a feasible alternative. Yet, estimating VF with regression models using fundus photographs fails to predict local vision loss, producing stationary nonsense predictions. To tackle this challenge, we propose a novel method for VF estimation that incorporates VF properties and is additionally regularized by an auxiliary task. Specifically, we first formulate VF estimation as an ordinal classification problem, where each VF point is interpreted as an ordinal variable rather than a continuous one, given that any VF point is a discrete integer with a relative ordering. Besides, we introduce an auxiliary task for MM severity classification to assist the generalization of VF estimation, as MM is strongly associated with vision loss in HM. Our method outperforms conventional regression by 16.61% in MAE metric on a real-world dataset. Moreover, our method is the first work for VF estimation using fundus photographs in HM, allowing for more convenient and accurate detection of vision loss in HM, which could be useful for not only clinics but also large-scale vision screenings.