Artificial intelligence performance in image-based ovarian cancer identification: A systematic review and meta-analysis.

ABSTRACT:

Background

Accurate identification of ovarian cancer (OC) is of paramount importance in clinical treatment success. Artificial intelligence (AI) is a potentially reliable assistant for the medical imaging recognition. We systematically review articles on the diagnostic performance of AI in OC from medical imaging for the first time.

Methods

The Medline, Embase, IEEE, PubMed, Web of Science, and the Cochrane library databases were searched for related studies published until August 1, 2022. Inclusion criteria were studies that developed or used AI algorithms in the diagnosis of OC from medical images. The binary diagnostic accuracy data were extracted to derive the outcomes of interest: sensitivity (SE), specificity (SP), and Area Under the Curve (AUC). The study was register

SUBMITTER: Xu HL 

PROVIDER: S-EPMC9486055 | biostudies-literature | 2022 Nov

REPOSITORIES: biostudies-literature