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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.
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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
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Inflammatory Bowel Disease II: Crohn's Disease01:30

Inflammatory Bowel Disease II: Crohn's Disease

Introduction
Inflammatory bowel disease, commonly known as IBD, refers to a collection of disorders that lead to persistent inflammation of the gastrointestinal tract. The two types of IBD are ulcerative colitis, which impacts the colon, and Crohn's disease, which can involve any part of the gastrointestinal segment.
Crohn's disease
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Related Experiment Video

Updated: May 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Automatic Localization and Classification of Crohn's Disease Activity in Computed Tomography Enterography Images

Peipei Wang1, Yu Liu2, Yuanjun Wang3

  • 1School of Health Science and Engineering, University of Shanghai for Science and Technology, Yangpu District, 516 Jungong Road, Shanghai, 200093, China.

Journal of Imaging Informatics in Medicine
|May 26, 2026
PubMed
Summary
This summary is machine-generated.

A new AI model, CD-YOLO, accurately detects and grades Crohn's disease (CD) activity on CT enterography scans. This tool improves diagnostic efficiency for radiologists and gastroenterologists.

Keywords:
Computed tomography enterographyCrohn’s diseaseDeep learningMedical image detection

Related Experiment Videos

Last Updated: May 28, 2026

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography
04:48

Application of Deep Learning-Based Medical Image Segmentation via Orbital Computed Tomography

Published on: November 30, 2022

Area of Science:

  • Artificial Intelligence in Medical Imaging
  • Gastroenterology
  • Radiology

Background:

  • Accurate localization and grading of Crohn's disease (CD) lesions on computed tomography enterography (CTE) are crucial for efficient diagnosis.
  • Challenges in CD lesion detection include variable sizes, shapes, and blurred boundaries on CTE images.

Purpose of the Study:

  • To develop an automated detection and classification model for CD lesions using a one-stage YOLOv5-based framework (CD-YOLO).
  • To enhance feature extraction and multi-scale feature fusion for improved detection of CD lesions with varying characteristics.

Main Methods:

  • Development of CD-YOLO, a one-stage model based on YOLOv5, incorporating specialized modules for feature extraction and multi-scale fusion.
  • Retrospective collection and analysis of CTE images from 233 pathologically and endoscopically confirmed CD patients.
  • Performance evaluation using mAP@0.5, precision, recall, and visualization via Gradient-weighted Class Activation Mapping (Grad-CAM).

Main Results:

  • CD-YOLO achieved mAP@0.5 scores of 92.1% for active CD and 84.2% for remission CD.
  • The model demonstrated significant improvements over YOLOv5s, with mAP@0.5 increasing by 2.0% for active and 4.9% for remission CD.
  • Missed detection and misdetection rates for CD lesions were reduced by 4.9% and 4.18%, respectively.

Conclusions:

  • The CD-YOLO framework shows superior performance in localizing and classifying CD lesion activity on CTE.
  • CD-YOLO has the potential to serve as an effective computer-aided diagnostic tool for radiologists and gastroenterologists.
  • The model can aid in clinical teaching and improve the overall diagnostic workflow for Crohn's disease.