Deep Learning in Neuroradiology: A Systematic Review of Current Algorithms and Approaches for the New Wave of Imaging Technology.

ABSTRACT:

Purpose

To systematically review and synthesize the current literature and to develop a compendium of technical characteristics of existing deep learning applications in neuroradiology.

Materials and methods

A Preferred Reporting Items for Systematic Reviews and Meta-Analyses systematic review was conducted through September 1, 2019, using PubMed, Cochrane, and Web of Science databases. A total of 155 articles discussing deep learning applications in neuroimaging were identified, divided by imaging modality, and characterized by imaging task, data source, algorithm type, and outcome metrics.

Results

A total of 155 studies were identified and divided into: MRI (n = 115), functional MRI (n = 19), CT (n = 9), PET (n = 18), and US (n = 1

SUBMITTER: Yao AD 

PROVIDER: S-EPMC8017426 | biostudies-literature | 2020 Mar

REPOSITORIES: biostudies-literature