Artificial intelligence for ovarian cancer diagnosis via ultrasound: a systematic review and quantitative assessment of model performance.

ABSTRACT:

Background

Early and accurate detection of ovarian cancer (OC) remains clinically challenging, prompting exploration of artificial intelligence (AI)-based ultrasound diagnostics. This systematic review and meta-analysis critically evaluate diagnostic accuracy, methodological rigor, and clinical applicability of AI models for ovarian mass classification using B-mode ultrasound.

Methods

A systematic literature search following PRISMA guidelines was conducted in PubMed, IEEE Xplore, and Scopus up to December 2024. Eligible studies included AI-based ovarian mass classification using B-mode ultrasound, reporting accuracy, sensitivity, specificity, and/or area under the ROC curve (AUC). Data extraction, quality assessment (PROBAST), and meta-analysis (random effects) were independ

SUBMITTER: Garcia-Atutxa I 

PROVIDER: S-EPMC12627067 | biostudies-literature | 2025

REPOSITORIES: biostudies-literature