AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.
Authors
Affiliations (10)
Affiliations (10)
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, USA.
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA.
- Section of Cardiovascular Medicine, Department of Internal Medicine, Yale School of Medicine, New Haven, CT, USA.
- Cardiovascular Data Science (CarDS) Lab, Yale School of Medicine, New Haven, CT, USA.
- Center for Outcomes Research and Evaluation, Yale-New Haven Hospital, New Haven, CT, USA.
- Section of Health Informatics, Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA.
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA.
- Department of Medicine, Case Western Reserve University School of Medicine, Cleveland, OH, USA. [email protected].
- University Hospitals Cleveland Medical Center, Cleveland, OH, USA. [email protected].
- Harrington Heart and Vascular Institute, University Hospitals, Cleveland, OH, USA. [email protected].
Abstract
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 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. 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.