Estimation of cycloplegic spherical refraction from non-cycloplegic clinical and biometric data in children using machine learning: a retrospective pilot study for screening triage.

objectiveTo develop and evaluate non-cycloplegic clinical and biometric models for estimating cycloplegic spherical power (DS), spherical equivalent (SE), and astigmatic power-vector components (J0 and J45) in children, and to assess their utility as a preliminary screening-triage aid rather than a substitute for cycloplegic refraction.

methodsThis retrospective pilot study included 590 children aged 2.4-14.8 years who underwent cycloplegic refraction at the Children's Hospital, Zhejiang University School of Medicine. The children were randomly divided at the patient level into training and held-out test sets at a ratio of 7:3, ensuring that both eyes from the same child remained in the same set. Astigmatism was expressed using J0 and J45. Power-vector components were calculated separately for non-cycloplegic and post-cycloplegic refraction, and post-cycloplegic J0/J45 served as prediction targets. Seven algorithms were screened; DT, BPNN, GPR, and LR were selected for clinically oriented evaluation using regression metrics, error thresholds, screening indices, subgroup analyses, GEE analysis, and Bland-Altman plots.

resultsAge, axial length, corneal astigmatism, corneal power, and UCVA were the main predictors for spherical refraction. After patient-level splitting, DT, BPNN, GPR, and LR showed moderate performance for DS and SE. DT achieved RMSE/R

conclusionsNon-cycloplegic clinical and biometric variables provided moderate estimates of cycloplegic DS and SE after patient-level data partitioning. The J0/J45 vector analysis indicated partial predictability of J0, whereas J45 prediction was clinically unreliable. This approach should be regarded as an exploratory triage aid for spherical refractive estimation rather than a substitute for cycloplegic refraction.