Systematic review of research design and reporting of imaging studies applying convolutional neural networks for radiological cancer diagnosis.

ABSTRACT:

Objectives

To perform a systematic review of design and reporting of imaging studies applying convolutional neural network models for radiological cancer diagnosis.

Methods

A comprehensive search of PUBMED, EMBASE, MEDLINE and SCOPUS was performed for published studies applying convolutional neural network models to radiological cancer diagnosis from January 1, 2016, to August 1, 2020. Two independent reviewers measured compliance with the Checklist for Artificial Intelligence in Medical Imaging (CLAIM). Compliance was defined as the proportion of applicable CLAIM items satisfied.

Results

One hundred eighty-six of 655 screened studies were included. Many studies did not meet the criteria for current design and reporting guidelines. Twenty-seven percent of studies docu

SUBMITTER: O'Shea RJ 

PROVIDER: S-EPMC8452579 | biostudies-literature | 2021 Oct

REPOSITORIES: biostudies-literature