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

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SIGNIFICANCE: Machine learning random forest algorithms were used to predict objective refractive outcomes after cycloplegic refraction using noncycloplegic clinical data. A classification model predicted post-cycloplegic myopia and could be useful in screening, and a second regression model predicted post-cycloplegic refractive and could provide a useful objective starting point in noncycloplegic subjective refractions. PURPOSE: A classification model sought to predict post-cycloplegic myopia using noncycloplegic clinical data to enhance myopia screening accuracy, whereas the regression model looked to predict objective refraction outcomes after cycloplegia for use as a starting point for noncycloplegic subjective refraction. METHODS: A cross-sectional study included data from 2483 eyes. Pre-refraction measurements, such as uncorrected visual acuity, axial length, and corneal curvature radius, were recorded. After cycloplegia, the spherical equivalent was measured. Random forest-based classification and regression models were established with input variables including age, gender, axial length, corneal curvature radius, axial length-to-corneal curvature radius ratio, spherical equivalent, and uncorrected visual acuity. Model performance was assessed using various metrics. RESULTS: The random forest classification model achieved high out-of-bag validation accuracy (92%), cross-validation accuracy (93%), external validation accuracy (94%), and precision (95%). The external validation sensitivity was 93%, and specificity was 95%. The regression model internal validation showed an out-of-bag validation R2 of 0.86, root mean square error (RMSE) of 0.66, and mean absolute error of 0.49. The 10-fold cross-validation R2 was 0.87, the RMSE was 0.64, and the mean absolute error was 0.48. In the external validation, R2 was 0.88, the RMSE was 0.63, and the mean absolute error was 0.48. CONCLUSIONS: 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.

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