SynthesisAbdominal radiology (New York)2026
Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI.
Synthesis in Abdominal radiology (New York), 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
objectiveThis 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.
methodsA 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.
resultsOf 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 methods are advancing in generalizability. Key gaps include standardized longitudinal pipelines, multi-vendor harmonization, and MASLD-specific fibrosis validation.
conclusionImaging AI for MASLD/MASH is converging on physics-consistent, automated quantitative mapping. Deployment readiness is highest for CT attenuation and MRI PDFF/R2* pipelines. Future work requires prospective, multicenter validation integrating physics-aware design, rigorous confounder control, and standardized reporting to enable reliable clinical integration.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.