Machine learning and deep learning algorithms in stroke medicine: a systematic review of hemorrhagic transformation prediction models.

ABSTRACT:

Background

Acute ischemic stroke (AIS) is a major cause of morbidity and mortality, with hemorrhagic transformation (HT) further worsening outcomes. Traditional scoring systems have limited predictive accuracy for HT in AIS. Recent research has explored machine learning (ML) and deep learning (DL) algorithms for stroke management. This study evaluates and compares the effectiveness of ML and DL algorithms in predicting HT post-AIS, benchmarking them against conventional models.

Methods

A systematic search was conducted across PubMed, Embase, Web of Science, Scopus, and IEEE, initially yielding 1421 studies. After screening, 24 studies met the inclusion criteria. The Prediction Model Risk of Bias Assessment Tool (PROBAST) was used to assess the quality of these studies, and a

SUBMITTER: Issaiy M 

PROVIDER: S-EPMC11638292 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature