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.