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Purpose of Review <p>This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) risk assessment. It addresses key questions regarding AI’s capabilities in risk prediction, segmentation, and integration with large volume data for CVD risk assessment.</p> Recent Findings <p>Recent studies demonstrate that AI, powered by deep learning models, significantly improve VAT and ectopic fat segmentation. AI can also be used to facilitate early detection of cardiometabolic risks and allows integration of imaging with clinical data for a more personalized approach to medicine. Emerging applications include AI-enabled telehealth and continuous monitoring through wearable technologies.</p> Summary <p>AI is transforming VAT and ectopic fat imaging by enabling more precise, personalized, and scalable assessments of fat distribution and cardiovascular risk. While challenges remain, such as model interpretability, future research will likely focus on refining algorithms and expanding AI’s clinical applications, potentially redefining obesity and CVD risk management.</p>

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AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2025年10月9日

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  • Sneha R. Kandi,
  • Rohan Khera,
  • Sanjay Rajagopalan,
  • Ian J. Neeland

摘要

Purpose of Review

This review explores the role of artificial intelligence (AI) in visceral adipose tissue (VAT) and ectopic fat imaging. It aims to evaluate how AI may be used to enhance the efficiency and accuracy of cardiovascular disease (CVD) risk assessment. It addresses key questions regarding AI’s capabilities in risk prediction, segmentation, and integration with large volume data for CVD risk assessment.

Recent Findings

Recent studies demonstrate that AI, powered by deep learning models, significantly improve VAT and ectopic fat segmentation. AI can also be used to facilitate early detection of cardiometabolic risks and allows integration of imaging with clinical data for a more personalized approach to medicine. Emerging applications include AI-enabled telehealth and continuous monitoring through wearable technologies.

Summary

AI is transforming VAT and ectopic fat imaging by enabling more precise, personalized, and scalable assessments of fat distribution and cardiovascular risk. While challenges remain, such as model interpretability, future research will likely focus on refining algorithms and expanding AI’s clinical applications, potentially redefining obesity and CVD risk management.