Machine and deep learning for MRI-based quantification of liver iron overload: a systematic review and meta-analysis
Abstract
Background: Liver iron overload, associated with conditions such as hereditary hemochromatosis and β‑thalassemia major, requires accurate quantification of liver iron concentration (LIC) to guide timely interventions and prevent complications. Magnetic resonance imaging (MRI) is the gold standard for noninvasive LIC assessment, but challenges in protocol variability and diagnostic consistency persist. Machine learning (ML) and deep learning (DL) offer potential to enhance MRI-based LIC quantification, yet their efficacy remains underexplored.
Objective: This systematic review and meta-analysis evaluates the diagnostic accuracy, algorithmic performance, and clinical applicability of ML and DL techniques for MRI-based LIC quantification in liver iron overload, adhering to PRISMA guidelines.
Methods: A comprehensive search across PubMed, Embase, Scopus, Web of Science, Cochrane Library, and IEEE Xplore identified studies applying ML/DL to MRI-based LIC quantification. Eligible studies were assessed for diagnostic accuracy (sensitivity, specificity, AUC), LIC quantification precision (correlation, mean absolute error), and clinical applicability (automation, processing time). Methodological quality was evaluated using the QUADAS‑2 tool, with qualitative synthesis and meta-analysis where feasible.
Results: Eight studies were included, employing algorithms such as convolutional neural networks (CNNs), radiomics, and fuzzy C‑mean clustering on T2*-weighted and multiparametric MRI.…
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Evidence Hub reads this as a meta-analysis, done in computer models (in silico), not in people. Its evidence level is 5 of 5 (Very low · case / narrative / animal / in vitro / in silico). Case reports, narrative reviews, and animal, cell or computer-model studies give the weakest evidence for human health.
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