AIMC Topic: Sarcopenia

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Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia.

Journal of healthcare engineering
The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the ...

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...

Use of artificial intelligence in the imaging of sarcopenia: A narrative review of current status and perspectives.

Nutrition (Burbank, Los Angeles County, Calif.)
Sarcopenia is a muscle disease which previously was associated only with aging, but in recent days it has been gaining more attention for its predictive value in a vast range of conditions and its potential link with overall health. Up to this point,...

Identifying sarcopenia in advanced non-small cell lung cancer patients using skeletal muscle CT radiomics and machine learning.

Thoracic cancer
BACKGROUND: Sarcopenia has been confirmed as a poor prognostic indicator of lung cancer. However, the lack of abdominal computed tomography (CT) hindered the application to assess the status of sarcopenia. The purpose of this study was to assess the ...

Abdominal musculature segmentation and surface prediction from CT using deep learning for sarcopenia assessment.

Diagnostic and interventional imaging
PURPOSE: The purpose of this study was to build and train a deep convolutional neural networks (CNN) algorithm to segment muscular body mass (MBM) to predict muscular surface from a two-dimensional axial computed tomography (CT) slice through L3 vert...

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...

Prognostic networks for unraveling the biological mechanisms of Sarcopenia.

Mechanisms of ageing and development
Sarcopenia is an age-related multifactorial process that involved several biological mechanisms, whose specific contribution and interplay is still unknown. The present study proposes prognostic networks based on machine learning approaches to unrave...

Machine Learning for Automatic Paraspinous Muscle Area and Attenuation Measures on Low-Dose Chest CT Scans.

Academic radiology
RATIONALE AND OBJECTIVES: To develop and evaluate an automated machine learning (ML) algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) scans to evaluate for presence of sarcopenia.

Sarcopenia: Beyond Muscle Atrophy and into the New Frontiers of Opportunistic Imaging, Precision Medicine, and Machine Learning.

Seminars in musculoskeletal radiology
As populations continue to age worldwide, the impact of sarcopenia on public health will continue to grow. The clinically relevant and increasingly common diagnosis of sarcopenia is at the confluence of three tectonic shifts in medicine: opportunisti...