Performance of Automated Machine Learning in Predicting Outcomes of Pneumatic Retinopexy.

ABSTRACT:

Purpose

Automated machine learning (AutoML) has emerged as a novel tool for medical professionals lacking coding experience, enabling them to develop predictive models for treatment outcomes. This study evaluated the performance of AutoML tools in developing models predicting the success of pneumatic retinopexy (PR) in treatment of rhegmatogenous retinal detachment (RRD). These models were then compared with custom models created by machine learning (ML) experts.

Design

Retrospective multicenter study.

Participants

Five hundred and thirty nine consecutive patients with primary RRD that underwent PR by a vitreoretinal fellow at 6 training hospitals between 2002 and 2022.

Methods

We used 2 AutoML platforms: MATLAB Classification Learner and Google Cloud AutoML. A

SUBMITTER: Nisanova A 

PROVIDER: S-EPMC11141253 | biostudies-literature | 2024 Sep-Oct

REPOSITORIES: biostudies-literature