Deep learning-based breast cancer diagnosis in breast MRI: systematic review and meta-analysis.

ABSTRACT:

Objectives

The aim of this work is to evaluate the performance of deep learning (DL) models for breast cancer diagnosis with MRI.

Materials and methods

A literature search was conducted on Web of Science, PubMed, and IEEE Xplore for relevant studies published from January 2015 to February 2024. The study was registered with the PROSPERO International Prospective Register of Systematic Reviews (protocol no. CRD42024485371). The quality assessment of diagnostic accuracy studies-2 (QUADAS2) tool and the Must AI Criteria-10 (MAIC-10) checklist were used to assess quality and risk of bias. The meta-analysis included studies reporting DL for breast cancer diagnosis and their performance, from which pooled summary estimates for the area under the curve (AUC), sensitivity, and speci

SUBMITTER: Abdullah KA 

PROVIDER: S-EPMC12226709 | biostudies-literature | 2025 Feb

REPOSITORIES: biostudies-literature