AIMC Topic: Trabeculectomy
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Machine learning-assisted prediction of trabeculectomy outcomes among patients of juvenile glaucoma by using 5-year follow-up data.
Indian journal of ophthalmology
Mar 8, 2024
OBJECTIVE: To develop machine learning (ML) models, using pre and intraoperative surgical parameters, for predicting trabeculectomy outcomes in the eyes of patients with juvenile-onset primary open-angle glaucoma (JOAG) undergoing primary surgery.
Deep learning-based identification of eyes at risk for glaucoma surgery.
Scientific reports
Jan 5, 2024
To develop and evaluate the performance of a deep learning model (DLM) that predicts eyes at high risk of surgical intervention for uncontrolled glaucoma based on multimodal data from an initial ophthalmology visit. Longitudinal, observational, retro...
A deep learning approach to investigate the filtration bleb functionality after glaucoma surgery: a preliminary study.
Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie
Aug 2, 2023
PURPOSE: To distinguish functioning from failed filtration blebs (FBs) implementing a deep learning (DL) model on slit-lamp images.
Evaluation of machine learning algorithms for trabeculectomy outcome prediction in patients with glaucoma.
Scientific reports
Feb 15, 2022
The purpose of this study was to evaluate the performance of machine learning algorithms to predict trabeculectomy surgical outcomes. Preoperative systemic, demographic and ocular data from consecutive trabeculectomy surgeries from a single academic ...
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