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Related Concept Videos

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

Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy

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This lesson explores three gastrointestinal imaging techniques: radionuclide testing, colonic transit studies, and virtual colonoscopy.
Radionuclide Testing
Radionuclide testing is a sophisticated medical technique for assessing gastrointestinal motility. It focuses on gastric emptying and colonic transit time. Radioactive markers track the movement of food through the digestive system, providing insights into gastrointestinal disorders.
In gastric emptying studies, a meal's liquid and...
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Related Experiment Video

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Multimodality Diagnosis of Mesenteric Ischemia
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Unsupervised and quantitative intestinal ischemia detection using conditional adversarial network in multimodal

Yaning Wang1, Laura Tiusaba2, Shimon Jacobs2

  • 1Johns Hopkins University, Department of Electrical and Computer Engineering, Baltimore, Maryland, United States.

Journal of Medical Imaging (Bellingham, Wash.)
|December 5, 2022
PubMed
Summary

A new deep learning model using laser speckle contrast imaging (LSCI) and a visible-light camera can objectively assess bowel perfusion during surgery. This method accurately identifies ischemic regions, improving diagnostic accuracy for gastrointestinal surgeries.

Keywords:
deep learningdye-free tissue perfusion assessmentgenerative adversarial networklaser speckle contrast imagingmultimodal optical imagingunsupervised anomaly detection

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastroenterology

Background:

  • Intraoperative assessment of bowel perfusion relies on subjective evaluation.
  • Objective and quantitative methods for assessing intestinal viability are limited.
  • Accurate assessment of bowel perfusion is critical for successful intestinal anastomosis.

Purpose of the Study:

  • To develop an objective, quantitative method for evaluating bowel perfusion.
  • To identify abnormal tissue perfusion regions during surgery.
  • To address the clinical need for improved bowel viability assessment.

Main Methods:

  • A dual-modality imaging system combining red-green-blue (RGB) and dye-free laser speckle contrast imaging (LSCI) was used.
  • A conditional generative adversarial network (cGAN) deep learning model was trained on swine model data.
  • The cGAN model performed image alignment and learned normal perfusion patterns for unsupervised detection of abnormalities.

Main Results:

  • The cGAN model accurately segmented ischemic intestine images with a Dice coefficient of 90.77% and accuracy of 93.06%.
  • The model processed images rapidly (0.05 seconds per image).
  • The unsupervised approach eliminated the need for subjective manual annotations.

Conclusions:

  • The proposed cGAN offers pixel-wise, dye-free quantitative analysis of intestinal perfusion.
  • This method supplements traditional LSCI techniques, enhancing diagnostic capabilities.
  • It has the potential to improve intraoperative diagnosis and clinical outcomes in gastrointestinal surgeries.