CompletedNot Applicable

Machine Learning Prediction of 1-Year Refractive Error After SMILE

China1,100 participantsStarted 2018-09-01

Plain-language summary

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.

Who can participate

Age range18 Years – 50 Years

SexALL

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Inclusion Criteria: * Eyes that underwent SMILE for correction of myopia or myopic astigmatism * Availability of preoperative examination data and surgical planning data * Availability of postoperative follow-up data at approximately 1 year * Age ≥ 18 years Exclusion Criteria: * Previous ocular surgery in the study eye * Ocular comorbidity likely to affect refractive or visual outcome * Intraoperative or postoperative complications that precluded standard outcome assessment * Missing key variables required for analysis

What they're measuring

Mean Absolute Error of Predicted 1-Year Postoperative Spherical Equivalent Prediction Error

Timeframe: 1 year after SMILE

Trial details

NCT IDNCT07478419

SponsorTongji University

Sponsor typeOTHER

Study typeOBSERVATIONAL

Primary completion2025-12-31

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