Diagnostic accuracy of deep learning-enhanced MRI techniques for liver fibrosis and cirrhosis detection: a systematic review and meta-analysis

Abstract Background Liver fibrosis and cirrhosis require accurate, noninvasive diagnostic methods. Deep learning-enhanced magnetic resonance imaging (MRI) modalities, including T2-weighted imaging, gadoxetic acid-enhanced MRI, and magnetic resonance elastography (MRE), have shown promise in improving diagnostic accuracy. Objective To systematically review and evaluate the diagnostic accuracy of deep learning-enhanced MRI modalities for liver fibrosis and cirrhosis detection, using liver biopsy as the reference standard, and to perform a meta-analysis of the diagnostic accuracy. Methods A systematic search was conducted across multiple databases (PubMed, Cochrane Library, Embase, Scopus, Google Scholar, and IEEE Xplore). Studies reporting diagnostic accuracy metrics (sensitivity, specificity, area under the receiver operating characteristic (AUROC)) for deep learning-based MRI modalities in adults with chronic liver disease were included. A meta-analysis was performed for the studies providing sufficient data. Results Seven studies with 6,547 participants were included. MRE-based approaches exhibited the highest diagnostic accuracy (AUROC: 0.759–0.93). A meta-analysis of three studies with liver biopsy as the reference standard showed high sensitivity (0.80–0.91) and specificity (0.79–0.90), but model non-convergence prevented pooled estimates. Conclusion Deep learning-enhanced MRI, particularly MRE, shows promise for noninvasive detection of liver fibrosis and cirrhosis. Despite promising individual results, further validation, standardized protocols, and larger studies are needed to confirm its clinical utility.