Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis
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 sup...
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| Format: | Article |
| Language: | English |
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Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina
2025-07-01
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| Series: | Biomolecules & Biomedicine |
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| Online Access: | https://www.bjbms.org/ojs/index.php/bjbms/article/view/12774 |
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| author | Matej Pekar Marek Kantor Jakub Balusik Jan Hecko Piotr Branny |
| author_facet | Matej Pekar Marek Kantor Jakub Balusik Jan Hecko Piotr Branny |
| author_sort | Matej Pekar |
| collection | DOAJ |
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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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| format | Article |
| id | doaj-art-e291df23a5e44b9686214e310695aff4 |
| institution | DOAJ |
| issn | 2831-0896 2831-090X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Association of Basic Medical Sciences of Federation of Bosnia and Herzegovina |
| record_format | Article |
| series | Biomolecules & Biomedicine |
| spelling | doaj-art-e291df23a5e44b9686214e310695aff42025-08-20T03:17:09ZengAssociation of Basic Medical Sciences of Federation of Bosnia and HerzegovinaBiomolecules & Biomedicine2831-08962831-090X2025-07-0110.17305/bb.2025.12774Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysisMatej Pekar0https://orcid.org/0000-0001-6238-2276Marek Kantor1Jakub Balusik2Jan Hecko3https://orcid.org/0000-0003-2514-2059Piotr Branny4https://orcid.org/0009-0003-9471-6850Complex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Physiology, Faculty of Medicine, Masaryk University, Brno, Czech RepublicComplex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech RepublicComplex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech RepublicComplex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Faculty of Electrical Engineering and Computer Science, VSB-Technical University of Ostrava, Ostrava, Czech RepublicComplex Cardiovascular Center, Hospital AGEL Trinec-Podlesi, Trinec, Czech Republic; Cardiac Surgery, Faculty of Medicine, Palacky University, Olomouc, Czech Republic 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. https://www.bjbms.org/ojs/index.php/bjbms/article/view/12774Body compositionartificial intelligenceAIcomputed tomographyCTsarcopenia |
| spellingShingle | Matej Pekar Marek Kantor Jakub Balusik Jan Hecko Piotr Branny Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis Biomolecules & Biomedicine Body composition artificial intelligence AI computed tomography CT sarcopenia |
| title | Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis |
| title_full | Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis |
| title_fullStr | Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis |
| title_full_unstemmed | Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis |
| title_short | Beyond BMI: An opinion on the clinical value of AI-powered CT body composition analysis |
| title_sort | beyond bmi an opinion on the clinical value of ai powered ct body composition analysis |
| topic | Body composition artificial intelligence AI computed tomography CT sarcopenia |
| url | https://www.bjbms.org/ojs/index.php/bjbms/article/view/12774 |
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