Diagnosis accuracy of machine learning for idiopathic pulmonary fibrosis: a systematic review and meta-analysis.

ABSTRACT:

Background

The diagnosis of idiopathic pulmonary fibrosis (IPF) is complex, which requires lung biopsy, if necessary, and multidisciplinary discussions with specialists. Clinical diagnosis of the two ailments is particularly challenging due to the impact of interobserver variability. Several studies have endeavored to utilize image-based machine learning to diagnose IPF and its subtype of usual interstitial pneumonia (UIP). However, the diagnostic accuracy of this approach lacks evidence-based support.

Objective

We conducted a systematic review and meta-analysis to explore the diagnostic efficiency of image-based machine learning (ML) for IPF.

Data sources and methods

We comprehensively searched PubMed, Cochrane, Embase, and Web of Science databases up to August 24, 2

SUBMITTER: Cong L 

PROVIDER: S-EPMC12001705 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature