This retrospective single-center observational study is designed to develop and internally validate a multi-output machine learning model for predicting 1-year postoperative refractive prediction error after small incision lenticule extraction (SMILE). The primary modeling target is 1-year spherical equivalent prediction error. Secondary targets include J0 prediction error, J45 prediction error, and postoperative uncorrected distance visual acuity in logarithm of the minimum angle of resolution. A secondary objective is to use the prediction framework to derive individualized nomogram recommendations and to compare these recommendations with ZEISS 4.0 planning in a virtual treatment-planning analysis.
Study Type
OBSERVATIONAL
Enrollment
1,100
Shanghai Tenth People's Hospital
Shanghai, Shanghai Municipality, China
Mean Absolute Error of Predicted 1-Year Postoperative Spherical Equivalent Prediction Error
Model performance for prediction of 1-year postoperative spherical equivalent prediction error, assessed in diopters.
Time frame: 1 year after SMILE
Mean Absolute Error of Predicted 1-Year Postoperative J0 Prediction Error
Model performance for prediction of the cardinal astigmatic vector component (J0), assessed in diopters.
Time frame: 1 year after SMILE
Mean Absolute Error of Predicted 1-Year Postoperative J45 Prediction Error
Mean Absolute Error of Predicted 1-Year Postoperative J45 Prediction Error
Time frame: 1 year after SMILE
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