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Related Concept Videos

Computed Tomography01:10

Computed Tomography

Tomography refers to imaging by sections. Computed tomography (CT) is a non-invasive imaging technique that uses computers to analyze several cross-sectional X-rays to reveal minute details about structures in the body.
The technique was invented in the 1970s and is based on the principle that as X-rays pass through the body, they are absorbed or reflected at different levels. In the technique, a patient lies on a motorized platform while a computerized axial tomography (CAT) scanner rotates...
Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

Introduction: MRI and CT scans are crucial advancements in medical imaging techniques, playing a vital role in diagnosing conditions related to the gastrointestinal (GI) system. Each scan serves distinct purposes, targets specific areas, and requires unique nursing duties.
Description of the Procedures
Computed Tomography (CT) scan:
Computed Tomography (CT) scans use X-ray technology to generate detailed images of bones, organs, and tissues. During the scan, the patient lies on a moving table...
Imaging Studies for Cardiovascular System V: CT01:28

Imaging Studies for Cardiovascular System V: CT

Cardiac computed tomography (CT) scanning is an advanced cardiac imaging technique that utilizes CT technology, with or without intravenous (IV) contrast, to produce accurate cross-sectional virtual slices of specific areas of the heart, coronary circulation, and major blood vessels such as the aorta, pulmonary veins, and arteries. The computer processes these slices to generate three-dimensional images. Multidetector CT (MDCT) is a rapid form of CT scanning that captures multiple slices...
Radiological Investigation I: X-ray and CT01:30

Radiological Investigation I: X-ray and CT

Radiological investigations, including X-rays and computed tomography (CT) scans, are critical for diagnosing and evaluating various medical conditions. These imaging techniques provide valuable insights into the body's internal structures, aiding in the detection of abnormalities, assessment of disease progression, and development of treatment strategies. This article delves into two primary radiological investigations, chest X-rays and CT scans, outlining their purpose, procedures, and the...
X-ray Imaging01:24

X-ray Imaging

German physicist Wilhelm Röntgen (1845–1923) was experimenting with electrical current when he discovered that a mysterious and invisible "ray" would pass through his flesh but leave an outline of his bones on a screen coated with a metal compound. In 1895, Röntgen made the first durable record of the internal parts of a living human: an "X-ray" image (as it came to be called) of his wife’s hand. Scientists worldwide quickly began their own experiments with X-rays, and by 1900, X-ray was widely...

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Correction: Luca et al. Global and Regional Diagnostic Results of Progress Toward Cervical Cancer Elimination, According to the WHO Strategy: A Systematic Literature Review with Narrative Synthesis. <i>Diagnostics</i> 2026, <i>16</i>, 1224.

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Updated: Jun 13, 2026

Segmentation and Linear Measurement for Body Composition Analysis using Slice-O-Matic and Horos
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Radiomics-Based Machine Learning for Sarcopenia Detection in Abdominal and Low-Dose CT.

Soo-Been Kim1, Young Jae Kim2, Kwang Gi Kim3

  • 1Medical Devices R&D Center, Gachon University Gil Medical Center, Incheon 21565, Republic of Korea.

Diagnostics (Basel, Switzerland)
|June 12, 2026
PubMed
Summary
This summary is machine-generated.

Radiomics machine learning models can detect sarcopenia using low-dose CT scans, offering a valuable tool for opportunistic assessment in aging populations. This approach aids in identifying muscle loss without increasing radiation exposure.

Keywords:
abdominal CTlow-dose CTmachine learningradiomicssarcopenia

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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Published on: August 16, 2020

Area of Science:

  • Radiology
  • Artificial Intelligence
  • Gerontology

Background:

  • Sarcopenia, a progressive loss of muscle mass and function, is a growing concern due to global population aging.
  • Computed tomography (CT) is a standard for muscle assessment, but radiation exposure is a limitation.
  • Lower-dose imaging protocols are being explored to mitigate radiation risks.

Purpose of the Study:

  • To evaluate the efficacy of radiomics-based machine learning (ML) models for sarcopenia detection.
  • To compare model performance using standard-dose abdominal CT (APCT) and low-dose CT (LDCT).

Main Methods:

  • Radiomics features were extracted from segmented skeletal muscle on CT images.
  • Machine learning models, including logistic regression, support vector machine, and random forest, were developed.
  • Model performance was assessed via fivefold cross-validation.

Main Results:

  • The random forest model showed the highest performance, with an AUC of 0.720 for APCT and 0.692 for LDCT.
  • SHapley Additive exPlanations identified intensity-based radiomics features, such as TotalEnergy, as key predictors.
  • Radiomics features from LDCT demonstrated potential for sarcopenia detection.

Conclusions:

  • Radiomics analysis of LDCT images can provide valuable insights for sarcopenia detection.
  • LDCT is frequently used in clinical settings like lung cancer screening, enabling opportunistic sarcopenia assessment.
  • This approach may facilitate early identification and management of sarcopenia without additional radiation dose.