The value of machine learning approaches in the diagnosis of early gastric cancer: a systematic review and meta-analysis.

ABSTRACT:

Background

The application of machine learning (ML) for identifying early gastric cancer (EGC) has drawn increasing attention. However, there lacks evidence-based support for its specific diagnostic performance. Hence, this systematic review and meta-analysis was implemented to assess the performance of image-based ML in EGC diagnosis.

Methods

We performed a comprehensive electronic search in PubMed, Embase, Cochrane Library, and Web of Science up to September 25, 2022. QUADAS-2 was selected to judge the risk of bias of included articles. We did the meta-analysis using a bivariant mixed-effect model. Sensitivity analysis and heterogeneity test were performed.

Results

Twenty-one articles were enrolled. The sensitivity (SEN), specificity (SPE), and SROC of ML-based mode

SUBMITTER: Shi Y 

PROVIDER: S-EPMC10832162 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature