Diagnostic accuracy of deep learning in prediction of osteoporosis: a systematic review and meta-analysis.

ABSTRACT:

Background

Osteoporosis is one of the most common metabolic diseases that is characterized by a decrease in bone density and a loss of the quality of the bone structure. The use of deep learning in the prediction of osteoporosis can provide a non-invasive, cost-effective, and efficient approach. The aim of this study is to investigate the diagnostic accuracy of deep learning in the prediction of osteoporosis.

Methods

This is a systematic review and meta-analysis study that was conducted on the diagnostic accuracy of deep learning algorithms for predicting osteoporosis. A literature search was performed in electronic databases including PubMed, Elsevier, and Google Scholar to identify relevant articles until December 1, 2023. Articles were searched in databases by combining r

SUBMITTER: Amani F 

PROVIDER: S-EPMC11619613 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature