Machine Learning Models for Predicting Long-Term Visual Acuity in Highly Myopic Eyes

Abstract

ImportanceHigh myopia is a global concern due to its escalating prevalence and the potential risk of severe visual impairment caused by pathologic myopia. Using artificial intelligence to estimate future visual acuity (VA) could help clinicians to identify and monitor patients with a high risk of vision reduction in advance.ObjectiveTo develop machine learning models to predict VA at 3 and 5 years in patients with high myopia.Design, Setting, and ParticipantsThis retrospective, single-center, cohort study was performed on patients whose best-corrected VA (BCVA) at 3 and 5 years was known. The ophthalmic examinations of these patients were performed between October 2011 and May 2021. Thirty-four variables, including general information, basic ophthalmic information, and categories of myopic maculopathy based on fundus and optical coherence tomography images, were collected from the medical records for analysis.Main Outcomes and MeasuresRegression models were developed to predict BCVA at 3 and 5 years, and a binary classification model was developed to predict the risk of developing visual impairment at 5 years. The performance of models was evaluated by discrimination metrics, calibration belts, and decision curve analysis. The importance of relative variables was assessed by explainable artificial intelligence techniques.ResultsA total of 1616 eyes from 967 patients (mean [SD] age, 58.5 [14.0] years; 678 female [70.1%]) were included in this analysis. Findings showed that support vector machines presented the best prediction of BCVA at 3 years (R2 = 0.682; 95% CI, 0.625-0.733) and random forest at 5 years (R2 = 0.660; 95% CI, 0.604-0.710). To predict the risk of visual impairment at 5 years, logistic regression presented the best performance (area under the receiver operating characteristic curve = 0.870; 95% CI, 0.816-0.912). The baseline BCVA (logMAR odds ratio [OR], 0.298; 95% CI, 0.235-0.378; P < .001), prior myopic macular neovascularization (OR, 3.290; 95% CI, 2.209-4.899; P < .001), age (OR, 1.578; 95% CI, 1.227-2.028; P < .001), and category 4 myopic maculopathy (OR, 4.899; 95% CI, 1.431-16.769; P = .01) were the 4 most important predicting variables and associated with increased risk of visual impairment at 5 years.Conclusions and RelevanceStudy results suggest that developing models for accurate prediction of the long-term VA for highly myopic eyes based on clinical and imaging information is feasible. Such models could be used for the clinical assessments of future visual acuity.

Multimodal imaging of optic nerve head abnormalities in high myopia

“…Additionally, the distinct layer information inherent in OCT images facilitates the segmentation and analysis of choroidal sublayers utilizing methodologies such as U-Net and mask R-CNN (88)(89)(90). These features, encompassing geometric measurements and characteristic lesion regions, can even be processed to develop precise models for long-term visual acuity prediction in highly myopic eyes (91,92). By integrating findings from these modalities, clinicians can tailor treatment plans, monitor disease progression, and optimize visual outcomes for patients with ONH abnormalities in high myopia.…”

Forecasting Myopic Maculopathy Risk Over a Decade: Development and Validation of an Interpretable Machine Learning Algorithm

“… 16 – 18 A recent study underscored the effectiveness of ML in predicting visual acuity in highly myopic eyes, spotlighting its potential in high myopia management. 19 However, there remains a knowledge gap in the deployment of ML for predicting long-term MMD progression.…”