Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.
Abstract
Objective: This systematic review critically appraises the current landscape of physics-aware artificial intelligence (AI) in medical imaging for quantitative biomarker mapping in Metabolic dysfunction-associated steatotic liver disease (MASLD) and its progressive form, MASH. It focuses on deep learning and radiomics applications across ultrasound, CT, and MRI.
Methods: A PRISMA 2020-guided systematic review was conducted, searching PubMed, Scopus, and Web of Science from 2015 to 2025. Studies applying AI to imaging for automated segmentation, quantitative steatosis/iron/fibrosis mapping, or staging in MASLD/MASH were included. Data on technical approaches, physics-aware design, reference standards, performance, and deployment readiness were extracted and synthesized narratively.
Results: Of 842 identified records, 33 studies were included. MRI leads in biophysically-grounded fat/iron quantification (PDFF, R2*) using confounder-corrected sequences and automated whole-liver segmentation. CT excels in scalable, opportunistic steatosis assessment via fully automated volumetric attenuation and dual-energy virtual non-contrast, though performance is phase-dependent. Ultrasound bifurcates into physics-informed quantitative ultrasound (attenuation/backscatter) correlating with MRI-PDFF and B-mode deep learning for steatosis grading, with emerging domain-adaptation techniques. Evidence strength and external validation are most robust for CT and MRI automation, while ultrasound…
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