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Objective <p>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.</p> Methods <p>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.</p> Results <p>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 methods are advancing in generalizability. Key gaps include standardized longitudinal pipelines, multi-vendor harmonization, and MASLD-specific fibrosis validation.</p> Conclusion <p>Imaging 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.</p>

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Physics-aware imaging AI for quantitative MASLD biomarker mapping: a systematic review of deep learning and radiomics across ultrasound, CT, and MRI

  • 期刊论文

  • 发表日期：

    2025年11月26日

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  • Houshyar Maghsoudi,
  • Ahmad Khonche,
  • Reza Gereami,
  • Farshad Gharebakhshi

摘要

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 methods are advancing in generalizability. Key gaps include standardized longitudinal pipelines, multi-vendor harmonization, and MASLD-specific fibrosis validation.

Conclusion

Imaging 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.