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

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...

Full description

Saved in:
Bibliographic Details
Main Authors: Ramesh Venkatesh, Priyanka Gandhi, Ayushi Choudhary, Gaurang Sehgal, Kanika Godani, Shubham Darade, Rupal Kathare, Prathiba Hande, Vishma Prabhu, Jay Chhablani
Format: Article
Language:English
Published: Wolters Kluwer Medknow Publications 2025-01-01
Series:Indian Journal of Ophthalmology
Subjects:
machine learning
macular hole
outcomes
prediction
statistical models
Online Access:https://journals.lww.com/10.4103/IJO.IJO_1895_24
Tags: Add Tag
No Tags, Be the first to tag this record!

Internet

https://journals.lww.com/10.4103/IJO.IJO_1895_24

Similar Items