ArticleTranslational vision science & technology2026
Predicting Small Effective Optical Zone After SMILE via Multimodal Machine Learning Integrating Corneal Topography and Clinical Parameters.
Article in Translational vision science & technology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
0 citing papers in PubMed.
No citing paper in PubMed yet.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
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 macro F1 score. Results: In the primary validation set, the AACM-PP-Model achieved an AUROC of 0.897 (95% confidence interval [CI], 0.819-0.975) and macro F1 of 0.823. It outperformed the best parameter-only model (PP-CatBoost, AUROC of 0.885 [95% CI, 0.813-0.957], macro F1 of 0.821) and best image-only model (model based on suction-initiated, centering-checked frames and anterior axial curvature map [SIF-CCF-AACM-Model], AUROC of 0.837 [95% CI, 0.745-0.928], macro F1 of 0.612). Superiority of AACM-PP-Model over PP-CatBoost and SIF-CCF-AACM-Model was maintained in internal (AUROC 0.957 vs. 0.915 vs. 0.773) and external (0.863 vs. 0.814 vs. 0.660) test sets. Intraoperative frame models performed poorly (AUROC < 0.7 on all test sets). Conclusions: Multimodal machine learning using standard preoperative data can accurately predict small post-SMILE EOZ, demonstrating superior generalization to support preoperative decision-making. Translational Relevance: Objective preoperative risk stratification could aid in personalized surgical planning and help manage patient expectations regarding postoperative visual quality.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.