AIMC Topic: Body Composition

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Artificial Intelligence in the Evaluation of Body Composition.

Seminars in musculoskeletal radiology
Body composition entails the measurement of muscle and fat mass in the body and has been shown to impact clinical outcomes in various aspects of human health. As a result, the need is growing for reliable and efficient noninvasive tools to measure bo...

Automated body composition analysis of clinically acquired computed tomography scans using neural networks.

Clinical nutrition (Edinburgh, Scotland)
BACKGROUND & AIMS: The quantity and quality of skeletal muscle and adipose tissue is an important prognostic factor for clinical outcomes across several illnesses. Clinically acquired computed tomography (CT) scans are commonly used for quantificatio...

Body Composition Analysis of Computed Tomography Scans in Clinical Populations: The Role of Deep Learning.

Lifestyle genomics
BACKGROUND: Body composition is increasingly being recognized as an important prognostic factor for health outcomes across cancer, liver cirrhosis, and critically ill patients. Computed tomography (CT) scans, when taken as part of routine care, provi...

Automatic monitoring system for individual dairy cows based on a deep learning framework that provides identification via body parts and estimation of body condition score.

Journal of dairy science
Body condition score (BCS) is a common tool for indirectly estimating the mobilization of energy reserves in the fat and muscle of cattle that meets the requirements of animal welfare and precision livestock farming for the effective monitoring of in...

Deep learning for automated segmentation of pelvic muscles, fat, and bone from CT studies for body composition assessment.

Skeletal radiology
OBJECTIVE: To develop a deep convolutional neural network (CNN) to automatically segment an axial CT image of the pelvis for body composition measures. We hypothesized that a deep CNN approach would achieve high accuracy when compared to manual segme...

Automated Abdominal Segmentation of CT Scans for Body Composition Analysis Using Deep Learning.

Radiology
Purpose To develop and evaluate a fully automated algorithm for segmenting the abdomen from CT to quantify body composition. Materials and Methods For this retrospective study, a convolutional neural network based on the U-Net architecture was traine...

A Novel Extension to Fuzzy Connectivity for Body Composition Analysis: Applications in Thigh, Brain, and Whole Body Tissue Segmentation.

IEEE transactions on bio-medical engineering
Magnetic resonance imaging (MRI) is the non-invasive modality of choice for body tissue composition analysis due to its excellent soft-tissue contrast and lack of ionizing radiation. However, quantification of body composition requires an accurate se...

Bioimpedance and New-Onset Heart Failure: A Longitudinal Study of >500 000 Individuals From the General Population.

Journal of the American Heart Association
BACKGROUND: Heart failure constitutes a high burden on patients and society, but although lifetime risk is high, it is difficult to predict without costly or invasive testing. We aimed to establish new risk factors of heart failure, which potentially...