Supervised machine learning statistical models for visual outcome prediction in macular hole surgery: a single-surgeon, standardized surgery study.

Purpose: To evaluate the predictive accuracy of various machine learning (ML) statistical models in forecasting postoperative visual acuity (VA) outcomes following macular hole (MH) surgery using preoperative optical coherence tomography (OCT) parameters.

Methods: This retrospective study included 158 eyes (151 patients) with full-thickness MHs treated between 2017 and 2023 by the same surgeon and using the same intraoperative surgical technique. Data from electronic medical records and OCT scans were extracted, with OCT-derived qualitative and quantitative MH characteristics recorded.

Predicting macular hole surgery outcomes: Integrating preoperative OCT features with supervised machine learning statistical models.

Purpose: To evaluate various supervised machine learning (ML) statistical models to predict anatomical outcomes after macular hole (MH) surgery using preoperative optical coherence tomography (OCT) features.

Methods: This retrospective study analyzed OCT data from idiopathic MH eyes at baseline and at 1-month post-surgery. The dataset was split 80:20 between training and testing.

Comparative analysis of pre-operative findings and post-operative outcomes in primary and secondary macular holes at a tertiary eye hospital in South India.

Purpose: This study aimed to compare demographics, clinical characteristics, and post-surgical outcomes between idiopathic and secondary full-thickness macular holes (MHs).

Methods: A retrospective analysis of 348 eyes from 339 patients treated between June 2017 and December 2023 was conducted. The study included both idiopathic and secondary MHs, excluding cases where surgery was not performed or lacked sufficient follow-up.