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Related Concept Videos

Imaging Studies I: CT and MRI01:14

Imaging Studies I: CT and MRI

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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...
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Computed Tomography01:10

Computed Tomography

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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.
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Imaging Studies III: Computed Tomography01:27

Imaging Studies III: Computed Tomography

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DefinitionComputed Tomography (CT) of the genitourinary (GU) tract is a non-invasive imaging modality that utilizes X-rays and computer processing to generate detailed cross-sectional images of the urinary system, encompassing the kidneys, ureters, bladder, and adjacent structures such as the adrenal glands.PurposeCT scans of the GU tract serve several diagnostic and therapeutic purposes, including:Diagnosis of Urinary Tract Diseases: Detects kidney stones, tumors, cysts, and congenital...
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Related Experiment Video

Updated: Apr 24, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
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Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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Performance Comparison of Machine Learning Using Radiomic Features and CNN-Based Deep Learning in Benign and

Jong Chan Yeom1, So Hyun Park2, Young Jae Kim3

  • 1Department of Bio-Health Medical Engineering, Gil Medical Center, Gachon University, Incheon, Republic of Korea.

Journal of Imaging Informatics in Medicine
|June 2, 2025
PubMed
Summary

Deep learning models slightly outperform machine learning for classifying vertebral compression fractures (VCFs) on CT scans. This approach enhances diagnostic accuracy by analyzing textural heterogeneity and spatial patterns, aiding clinicians in distinguishing benign from malignant cases.

Keywords:
Contrast-enhanced computed tomographyDeep learningMachine learningRadiomicsVertebral compression fracture

Related Experiment Videos

Last Updated: Apr 24, 2026

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model
07:15

Machine Learning Algorithms for Early Detection of Bone Metastases in an Experimental Rat Model

Published on: August 16, 2020

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence in Medicine
  • Oncology

Background:

  • Distinguishing benign from malignant vertebral compression fractures (VCFs) is crucial for patient management.
  • Abdominal CT lacks the soft tissue contrast of MRI, making VCF classification challenging.
  • Radiomics and deep learning offer potential for automated VCF assessment using CT data.

Purpose of the Study:

  • To compare the performance of radiomic feature-based machine learning (ML) and convolutional neural network-based deep learning (DL) models for classifying VCFs on abdominal CT.
  • To evaluate the diagnostic accuracy and interpretability of both ML and DL approaches.

Main Methods:

  • A retrospective analysis of 447 VCFs (196 benign, 251 malignant) from 286 patients using abdominal CT scans.
  • Extraction of radiomic features using PyRadiomics, with Recursive Feature Elimination selecting six texture-based features.
  • Training and comparison of ML models (XGBoost, SVM, KNN, Random Forest) and a 3D Convolutional Neural Network (CNN).

Main Results:

  • The 3D CNN achieved marginally superior performance, with a statistically significant higher Area Under the Curve (AUC) of 77.66% compared to the top ML model (XGBoost) at 75.91%.
  • DL models demonstrated better precision, F1 score, and accuracy.
  • DL attention maps provided spatial interpretability, localizing diagnostically relevant regions, while radiomics offered quantifiable biomarkers of heterogeneity.

Conclusions:

  • Deep learning models show promise for enhancing diagnostic accuracy in VCF assessment using abdominal CT, offering improved performance and spatial interpretability over traditional radiomics.
  • Integrating ML and DL approaches may further improve diagnostic confidence and accuracy.
  • Future multi-center studies with histopathological validation are needed to generalize findings.