Purpose of the review We aim to review the methods, current research evidence, and future
directions in body composition analysis (BCA) with CT imaging. Recent findings CT images …

Automated computed tomography (CT) scan segmentation (labelling of pixels according to
tissue type) is now possible. This technique is being adapted to achieve three‐dimensional …

Objective We aimed to develop and validate a deep learning system for fully automated
segmentation of abdominal muscle and fat areas on computed tomography (CT) images …

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 …

CT body composition analysis has been shown to play an important role in predicting health
and has the potential to improve patient outcomes if implemented clinically. Recent …

Rationale and Objectives To develop and evaluate an automated machine learning (ML)
algorithm for segmenting the paraspinous muscles on chest computed tomography (CT) …

Background Quantitative muscle and fat data obtained through body composition analysis
are expected to be a new stable biomarker for the early and accurate prediction of treatment …

Background Survival in patients with metastatic colorectal cancer (mCRC) has been
associated with tumor mutational status, muscle loss, and weight loss. We sought to explore …

This study aimed to address the limitations of conventional methods for measuring skeletal
muscle mass for sarcopenia diagnosis by introducing an artificial intelligence (AI) system for …

Background Muscle quantification is an essential step in sarcopenia evaluation. Purpose To
develop and evaluate an automated machine learning (ML) algorithm for segmenting the …