Artificial intelligence fracture recognition on computed tomography: review of literature and recommendations.

ABSTRACT:

Purpose

The use of computed tomography (CT) in fractures is time consuming, challenging and suffers from poor inter-surgeon reliability. Convolutional neural networks (CNNs), a subset of artificial intelligence (AI), may overcome shortcomings and reduce clinical burdens to detect and classify fractures. The aim of this review was to summarize literature on CNNs for the detection and classification of fractures on CT scans, focusing on its accuracy and to evaluate the beneficial role in daily practice.

Methods

Literature search was performed according to the PRISMA statement, and Embase, Medline ALL, Web of Science Core Collection, Cochrane Central Register of Controlled Trials and Google Scholar databases were searched. Studies were eligible when the use of AI for the detect

SUBMITTER: Dankelman LHM 

PROVIDER: S-EPMC10175338 | biostudies-literature | 2023 Apr

REPOSITORIES: biostudies-literature