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Body Composition

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Detection of sarcopenic obesity and prediction of long-term survival in patients with gastric cancer using preoperative computed tomography and machine learning.

Journal of surgical oncology
BACKGROUND: Previous studies evaluating the prognostic value of computed tomography (CT)-derived body composition data have included few patients. Thus, we assessed the prevalence and prognostic value of sarcopenic obesity in a large population of ga...

Automated Measurements of Body Composition in Abdominal CT Scans Using Artificial Intelligence Can Predict Mortality in Patients With Cirrhosis.

Hepatology communications
Body composition measures derived from already available electronic medical records (computed tomography [CT] scans) can have significant value, but automation of measurements is needed for clinical implementation. We sought to use artificial intelli...

Deep neural network for automatic volumetric segmentation of whole-body CT images for body composition assessment.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: Body composition analysis on CT images is a valuable tool for sarcopenia assessment. We aimed to develop and validate a deep neural network applicable to whole-body CT images of PET-CT scan for the automatic volumetric segmentation...

Pixel-wise body composition prediction with a multi-task conditional generative adversarial network.

Journal of biomedical informatics
The analysis of human body composition plays a critical role in health management and disease prevention. However, current medical technologies to accurately assess body composition such as dual energy X-ray absorptiometry, computed tomography, and m...

Machine Learning in Body Composition Analysis.

European urology focus
Body composition analysis (BCA) generates objective anthropometric data that can inform prognostication and treatment decisions across a wide variety of urologic conditions. A patient's body composition, specifically muscle and adipose tissue mass, m...

Genetic architecture of 11 organ traits derived from abdominal MRI using deep learning.

eLife
Cardiometabolic diseases are an increasing global health burden. While socioeconomic, environmental, behavioural, and genetic risk factors have been identified, a better understanding of the underlying mechanisms is required to develop more effective...

Fully automated body composition analysis in routine CT imaging using 3D semantic segmentation convolutional neural networks.

European radiology
OBJECTIVES: Body tissue composition is a long-known biomarker with high diagnostic and prognostic value not only in cardiovascular, oncological, and orthopedic diseases but also in rehabilitation medicine or drug dosage. In this study, the aim was to...

Artificial intelligence-aided CT segmentation for body composition analysis: a validation study.

European radiology experimental
BACKGROUND: Body composition is associated with survival outcome in oncological patients, but it is not routinely calculated. Manual segmentation of subcutaneous adipose tissue (SAT) and muscle is time-consuming and therefore limited to a single CT s...