NCT07478419

Machine Learning Prediction of 1-Year Refractive Error After SMILE

Development and Internal Validation of a Multi-output Machine Learning Model for Predicting 1-Year Postoperative Refractive Prediction Error After Small Incision Lenticule Extraction and Comparative Virtual Planning Analysis Against ZEISS 4.0

• Status: Completed
• Phase: —
• Study type: Observational
• Enrollment: 1,100 (actual)
• Sponsor: Tongji University · Academic / Other
• Sex: All
• Age: 18 Years – 50 Years
• Healthy volunteers: Not accepted

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.

Conditions

• Myopia
• Myopic Astigmatism
• Refractive Error - Myopia Axial

Timeline

• Start date: 2018-09-01
• Primary completion: 2025-12-31
• Completion: 2026-01-31
• First posted: 2026-03-17
• Last updated: 2026-03-17

Locations

1 site across 1 country: China