Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis

Authors

  • Matej Pekar Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Physiology, Faculty of Medicine, Masaryk University, Brno, Czech Republic https://orcid.org/0000-0001-6238-2276
  • Marek Kantor Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic
  • Jakub Balusik Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic
  • Jan Hecko Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech Republic https://orcid.org/0000-0003-2514-2059
  • Piotr Branny Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Cardiac Surgery, Faculty of Medicine, Palacky University, Olomouc, Czech Republic https://orcid.org/0009-0003-9471-6850

DOI:

https://doi.org/10.17305/bb.2025.12774

Keywords:

Body composition, artificial intelligence, AI, computed tomography, CT, sarcopenia, visceral adipose tissue

Abstract

Body Mass Index (BMI) has long been used as a standard measure for assessing population-level health risks, but its clinical adequacy has increasingly been called into question. This opinion paper challenges the clinical adequacy of BMI and presents AI-enhanced CT body composition analysis as a superior alternative for individualized risk assessment. While BMI serves population-level screening, its inability to differentiate between tissue types leads to critical misclassifications, particularly for sarcopenic obesity. AI-powered analysis of CT imaging at the L3 vertebra level provides precise quantification of skeletal muscle index, visceral, and subcutaneous adipose tissues -metrics that consistently outperform BMI in predicting outcomes across oncology, cardiology, and critical care. Recent technological advances have transformed this approach: the "opportunistic" use of existing clinical CT scans eliminates radiation concerns, while AI automation has reduced analysis time from 15-20 minutes to mere seconds. These innovations effectively address previous implementation barriers and enable practical clinical application with minimal resource demands, creating opportunities for targeted interventions and personalized care pathways.

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Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis

Published

07-07-2025

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Special Article

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How to Cite

1.
Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis. Biomol Biomed [Internet]. 2025 Jul. 7 [cited 2025 Jul. 10];. Available from: https://www.bjbms.org/ojs/index.php/bjbms/article/view/12774