Deep Learning for Computed Tomography Assessment of Hepatic Fibrosis and Cirrhosis: A Systematic Review

Abstract

Studies were identified using deep learning artificial intelligence methods for the analysis of computed tomography images in the assessment of hepatic fibrosis and cirrhosis. A systematic review was conducted in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses of Diagnostic Test Accuracy Studies protocol to evaluate the accuracy of deep learning algorithms for this objective (PROSPERO CRD 42023366201). A literature search was conducted on Medline, Embase, Web of Science, and IEEE Xplore databases. The search was conducted with a timeline from January 1, 2000,through November 13, 2022. Our search resulted in 3877 studies for screening, which yielded 6 studies meeting our inclusion criteria. All studies were retrospective. Three studies performed internal validation, and 2 studies performed external validation. Four studies used image classification algorithms, whereas 2 studies used image segmentation algorithms to derive volumetric measurements of the liver and spleen. Accuracy of the algorithms was variable in diagnosing significant and advanced fibrosis and cirrhosis, with the area under the curve ranging from 0.63 to 0.97. Deep learning algorithms using computed tomography images have the potential to classify fibrosis stages. Heterogeneity in cohorts and methodologies limits the generalizability of these studies.