AI-based volumetric six-tissue body composition quantification from CT cardiac attenuation scans enhances mortality prediction: multicenter study.
Jirong YiAnna M MichalowskaAakash ShanbhagRobert J H MillerJolien GeersWenhao ZhangAditya KillekarNipun ManralMark LemleyMikolaj BuchwaldJacek KwiecinskiJianhang ZhouPaul B KavanaghJoanna X LiangValerie BuiloffTerrence D RuddyAndrew J EinsteinAttila FeherRobert J H MillerAlbert J SinusasDaniel S BermanDamini DeyPiotr J SlomkaPublished in: medRxiv : the preprint server for health sciences (2024)
The comprehensive body composition analysis can be routinely performed, at the point of care, for all cardiac perfusion scans utilizing CTAC. Automatically-obtained volumetric body composition quantification metrics provide added value over existing risk factors, using already-obtained scans to significantly improve the risk stratification of patients and clinical decision-making.
Keyphrases
- body composition
- computed tomography
- contrast enhanced
- resistance training
- risk factors
- bone mineral density
- dual energy
- decision making
- end stage renal disease
- left ventricular
- ejection fraction
- newly diagnosed
- magnetic resonance imaging
- positron emission tomography
- artificial intelligence
- type diabetes
- image quality
- machine learning
- patient reported outcomes
- data analysis