Predicting Small Effective Optical Zone After SMILE via Multimodal Machine Learning Integrating Corneal Topography and Clinical Parameters.

ABSTRACT:

Purpose

To develop and validate a machine learning system for the preoperative prediction of small effective optical zone (EOZ; diameter < 5.5 mm) after small incision lenticule extraction (SMILE).

Methods

In this multicenter cohort study, 1030 multimodal combinations of preoperative parameters (PP), anterior corneal curvature maps (AACM), surgery video frames, and three-month postoperative EOZ diameter from 1030 eyes (634 patients) undergoing SMILE were divided: 677 for training, 85 for primary validation, 85 for internal test, and 183 for external test. The AACM-PP-Model integrating AACM and PP was developed and compared against parameter-only or image-only models, with primary performance evaluated by the area under the receiver operating characteristic curve (AUROC) and

SUBMITTER: Xiong J 

PROVIDER: S-EPMC12927424 | biostudies-literature | 2026 Feb

REPOSITORIES: biostudies-literature