AIMC Topic: Body Composition
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Deep learning-quantified body composition from positron emission tomography/computed tomography and cardiovascular outcomes: a multicentre study.
European heart journal
Jun 23, 2025
BACKGROUND AND AIMS: Positron emission tomography (PET)/computed tomography (CT) myocardial perfusion imaging (MPI) is a vital diagnostic tool, especially in patients with cardiometabolic syndrome. Low-dose CT scans are routinely performed with PET f...
Visceral Fat Quantified by a Fully Automated Deep-Learning Algorithm and Risk of Incident and Recurrent Diverticulitis.
Diseases of the colon and rectum
Jun 1, 2025
BACKGROUND: Obesity is a risk factor for diverticulitis. However, it remains unclear whether visceral fat area, a more precise measurement of abdominal fat, is associated with the risk of diverticulitis.
Artificial intelligence generated 3D body composition predicts dose modifications in patients undergoing neoadjuvant chemotherapy for rectal cancer.
Journal of cancer research and clinical oncology
May 16, 2025
PURPOSE: Chemotherapy administration is a balancing act between giving enough to achieve the desired tumour response while limiting adverse effects. Chemotherapy dosing is based on body surface area (BSA). Emerging evidence suggests body composition ...
Fully volumetric body composition analysis for prognostic overall survival stratification in melanoma patients.
Journal of translational medicine
May 12, 2025
BACKGROUND: Accurate assessment of expected survival in melanoma patients is crucial for treatment decisions. This study explores deep learning-based body composition analysis to predict overall survival (OS) using baseline Computed Tomography (CT) s...
Artificial Intelligence-Assisted Muscular Ultrasonography for Assessing Inflammation and Muscle Mass in Patients at Risk of Malnutrition.
Nutrients
May 9, 2025
BACKGROUND: Malnutrition, influenced by inflammation, is associated with muscle depletion and body composition changes. This study aimed to evaluate muscle mass and quality using Artificial Intelligence (AI)-enhanced ultrasonography in patients with ...
Applying Artificial Intelligence to Quantify Body Composition on Abdominal CTs and Better Predict Kidney Transplantation Wait-List Mortality.
Journal of the American College of Radiology : JACR
Mar 1, 2025
BACKGROUND: Prekidney transplant evaluation routinely includes abdominal CT for presurgical vascular assessment. A wealth of body composition data are available from these CT examinations, but they remain an underused source of data, often missing fr...
Quantification of training-induced alterations in body composition via automated machine learning analysis of MRI images in the thigh region: A pilot study in young females.
Physiological reports
Feb 1, 2025
The maintenance of an appropriate ratio of body fat to muscle mass is essential for the preservation of health and performance, as excessive body fat is associated with an increased risk of various diseases. Accurate body composition assessment requi...
External validation of a deep learning model for automatic segmentation of skeletal muscle and adipose tissue on abdominal CT images.
The British journal of radiology
Dec 1, 2024
OBJECTIVES: Body composition assessment using CT images at the L3-level is increasingly applied in cancer research and has been shown to be strongly associated with long-term survival. Robust high-throughput automated segmentation is key to assess la...
Dissecting unique and common variance across body and brain health indicators using age prediction.
Human brain mapping
Apr 15, 2024
Ageing is a heterogeneous multisystem process involving different rates of decline in physiological integrity across biological systems. The current study dissects the unique and common variance across body and brain health indicators and parses inte...
Association Between Body Composition and Survival in Patients With Gastroesophageal Adenocarcinoma: An Automated Deep Learning Approach.
JCO clinical cancer informatics
Apr 1, 2024
PURPOSE: Body composition (BC) may play a role in outcome prognostication in patients with gastroesophageal adenocarcinoma (GEAC). Artificial intelligence provides new possibilities to opportunistically quantify BC from computed tomography (CT) scans...
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