Machine learning-based prediction of anatomical outcome after idiopathic macular hole surgery.

ABSTRACT:

Background

To develop a machine learning (ML) model for the prediction of the idiopathic macular hole (IMH) status at 1 month after vitrectomy and internal limiting membrane peeling (VILMP) surgery.

Methods

A total of 288 IMH eyes from four ophthalmic centers were enrolled. All eyes underwent optical coherence tomography (OCT) examinations upon admission and one month after VILMP. First, 1,792 preoperative macular OCT parameters and 768 clinical variables of 256 eyes from two ophthalmic centers were used to train and internally validate ML models. Second, 224 preoperative macular OCT parameters and 96 clinical variables of 32 eyes from the other two centers were utilized for external validation. To fulfill the purpose of predicting postoperative IMH status (i.e., closed or o

SUBMITTER: Xiao Y 

PROVIDER: S-EPMC8184483 | biostudies-literature | 2021 May

REPOSITORIES: biostudies-literature