Machine learning and acute stroke imaging.

ABSTRACT:

Background

In recent years, machine learning (ML) has had notable success in providing automated analyses of neuroimaging studies, and its role is likely to increase in the future. Thus, it is paramount for clinicians to understand these approaches, gain facility with interpreting ML results, and learn how to assess algorithm performance.

Objective

To provide an overview of ML, present its role in acute stroke imaging, discuss methods to evaluate algorithms, and then provide an assessment of existing approaches.

Methods

In this review, we give an overview of ML techniques commonly used in medical imaging analysis and methods to evaluate performance. We then review the literature for relevant publications. Searches were run in November 2021 in Ovid Medline and PubMed.

SUBMITTER: Sheth SA 

PROVIDER: S-EPMC10523646 | biostudies-literature | 2023 Feb

REPOSITORIES: biostudies-literature