Machine learning for endoscopic third ventriculostomy success prediction-a systematic review and meta-analysis.

ABSTRACT:

Background

Endoscopic third ventriculostomy (ETV) is a common treatment for pediatric obstructive hydrocephalus, but predicting its success remains challenging. Traditional predictive tools, such as the Endoscopic Third Ventriculostomy Success Score (ETVSS) and logistic regression (LR) models, are widely used; however, recent advancements in machine learning (ML) have shown promise in improving prediction accuracy. This systematic review and meta-analysis aim to evaluate the effectiveness of ML models in predicting ETV success and compare them to traditional models.

Methods

A systematic search across five databases was performed. Authors searched for studies, which used ML algorithms to predict ETV success. This review included studies included studies that reported the area

SUBMITTER: Lajczak A 

PROVIDER: S-EPMC12479695 | biostudies-literature | 2025 Sep

REPOSITORIES: biostudies-literature