{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"omics_type":["Unknown"],"volume":["15(2)"],"submitter":["Xiong J"],"pubmed_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 "],"journal":["Translational vision science & technology"],"pagination":["26"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC12927424"],"repository":["biostudies-literature"],"pubmed_title":["Predicting Small Effective Optical Zone After SMILE via Multimodal Machine Learning Integrating Corneal Topography and Clinical Parameters."],"pmcid":["PMC12927424"],"pubmed_authors":["Li W","Xiong J","Huang F","Ai C","Ma L","Zhang X","Ling L","Gao W","Dai D","Cen G","Gui F"],"additional_accession":[]},"is_claimable":false,"name":"Predicting Small Effective Optical Zone After SMILE via Multimodal Machine Learning Integrating Corneal Topography and Clinical Parameters.","description":"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 ","dates":{"release":"2026-01-01T00:00:00Z","publication":"2026 Feb","modification":"2026-07-16T17:45:16.025Z","creation":"2026-07-09T11:09:44.542Z"},"accession":"S-EPMC12927424","cross_references":{"pubmed":["41718660"],"doi":["10.1167/tvst.15.2.26"]}}