Journal of refractive surgery (Thorofare, N.J. : 1995) · 2026
Machine Learning-Based Prediction of LASIK Console Inputs for Aspheric Planning (Q-factor, Defocus, Astigmatism): A Translational Methods Study.
СтатьяНазвание и аннотация: Перевод в очереди
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Garnier Steven
Аннотация
PURPOSE: To frame aspheric laser refractive planning as the supervised prediction of console-programmable inputs (Defocus, Astigmatism, and Q-factor) and to benchmark competing regression models; this is a translational methods proof-of-concept, not a clinical effectiveness study.
METHODS: An anonymized, retrospective, single-platform dataset of 2,448 complete-case treatments was analyzed. Multi-output regressors (linear and nonlinear) were trained and compared using prespecified metrics (R2, MAE/MSE) and residual-distribution visualization/calibration. Actuator-response checks related programmed inputs to changes in Defocus (Z20) and primary spherical aberration (Z40). External validation used a temporally later, device-shift cohort (n = 147).
RESULTS: Linear regression predicted Defocus and astigmatism well (eg, Defocus R2 = 0.98) but degraded for Q-factor (R2 = 0.47), whereas nonlinear models improved Q-factor error and calibration. Actuator-response analyses showed strong coupling for Defocus input (R2 = 0.97), moderate coupling of Q-factor to ΔZ40 (R2 = 0.51), and a weak Q→Defocus cross-effect (R2 = 0.12). On external validation, the best model generalized: Defocus MAE 0.22 D (R2 = 0.98) and Q-factor MAE 0.21 (R2 = 0.81).
CONCLUSIONS: Supervised nonlinear multi-output models achieve lower error and better calibration for Q-factor than linear baselines, supporting a metric-driven pathway toward more reliable control of low-order refractive targets and primary asphericity. Potential clinical implications include tissue sparing, improved contrast, and near-vision gains. Prospective, human-in-the-loop evaluation with safety and patient-reported endpoints is warranted.
METHODS: An anonymized, retrospective, single-platform dataset of 2,448 complete-case treatments was analyzed. Multi-output regressors (linear and nonlinear) were trained and compared using prespecified metrics (R2, MAE/MSE) and residual-distribution visualization/calibration. Actuator-response checks related programmed inputs to changes in Defocus (Z20) and primary spherical aberration (Z40). External validation used a temporally later, device-shift cohort (n = 147).
RESULTS: Linear regression predicted Defocus and astigmatism well (eg, Defocus R2 = 0.98) but degraded for Q-factor (R2 = 0.47), whereas nonlinear models improved Q-factor error and calibration. Actuator-response analyses showed strong coupling for Defocus input (R2 = 0.97), moderate coupling of Q-factor to ΔZ40 (R2 = 0.51), and a weak Q→Defocus cross-effect (R2 = 0.12). On external validation, the best model generalized: Defocus MAE 0.22 D (R2 = 0.98) and Q-factor MAE 0.21 (R2 = 0.81).
CONCLUSIONS: Supervised nonlinear multi-output models achieve lower error and better calibration for Q-factor than linear baselines, supporting a metric-driven pathway toward more reliable control of low-order refractive targets and primary asphericity. Potential clinical implications include tissue sparing, improved contrast, and near-vision gains. Prospective, human-in-the-loop evaluation with safety and patient-reported endpoints is warranted.