Image-based AI diagnostic performance for fatty liver: a systematic review and meta-analysis.

ABSTRACT:

Background

The gold standard to diagnose fatty liver is pathology. Recently, image-based artificial intelligence (AI) has been found to have high diagnostic performance. We systematically reviewed studies of image-based AI in the diagnosis of fatty liver.

Methods

We searched the Cochrane Library, Pubmed, Embase and assessed the quality of included studies by QUADAS-AI. The pooled sensitivity, specificity, negative likelihood ratio (NLR), positive likelihood ratio (PLR), and diagnostic odds ratio (DOR) were calculated using a random effects model. Summary receiver operating characteristic curves (SROC) were generated to identify the diagnostic accuracy of AI models.

Results

15 studies were selected in our meta-analysis. Pooled sensitivity and specificity were 92% (95%

SUBMITTER: Zhao Q 

PROVIDER: S-EPMC10712108 | biostudies-literature | 2023 Dec

REPOSITORIES: biostudies-literature