Document Type
Article
Publication Date
10-9-2025
Abstract
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.
Keywords
artificial intelligence, cardiovascular risk, deep learning, ectopic fat, medical image segmentation, visceral adipose tissue
Language
English
Publication Title
Current Atherosclerosis Reports
Grant
R01HL167858
Rights
© 2025 The Author(s). This is an Open Access work distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Creative Commons License
This work is licensed under a Creative Commons Attribution 4.0 International License.
Recommended Citation
Kandi, S.R., Khera, R., Rajagopalan, S. et al. AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment. Curr Atheroscler Rep 27, 101 (2025). https://doi.org/10.1007/s11883-025-01356-1
Manuscript Version
Final Publisher Version