Dynamic Structural Recovery Parameters Enhance Prediction of Visual Outcomes After Macular Hole Surgery.

ABSTRACT:

Purpose

The purpose of this study was to introduce novel dynamic structural parameters and evaluate their integration within a multimodal deep learning (DL) framework for predicting postoperative visual recovery in patients with idiopathic full-thickness macular hole (iFTMH).

Methods

We utilized a publicly available longitudinal optical coherence tomography (OCT) dataset at five stages (preoperative, 2 weeks, 3 months, 6 months, and 12 months). A stage-specific segmentation model delineated related structures, and an automated pipeline extracted quantitative, composite, qualitative, and dynamic features. Binary logistic regression models, constructed with and without dynamic parameters, assessed their incremental predictive value for best-corrected visual acuity (BCVA). A mu

SUBMITTER: Zhao Y 

PROVIDER: S-EPMC12758438 | biostudies-literature | 2025 Dec

REPOSITORIES: biostudies-literature