Machine‐learning random forest algorithms predict post‐cycloplegic myopic corrections from noncycloplegic clinical data

By analyzing noncycloplegic clinical data, the classification model enables earlier detection of myopia, supporting timely intervention and management. The regression model aims to accurately predict post-cycloplegia myopic corrections, providing reliable initial data for subjective refraction. This could help optometrists perform noncycloplegic subjective refraction more efficiently and is particularly relevant in China, where retinoscopy is not yet fully popularized and many school students decline cycloplegic refraction due to academic pressures and limited free time, primarily because it requires a follow-up the next day.