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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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Central Reading of Ulcerative Colitis Clinical Trial Videos Using Neural Networks.

Klaus Gottlieb1, James Requa2, William Karnes2

  • 1Eli Lilly and Company, Indianapolis, Indiana.

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|October 25, 2020
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A new machine learning algorithm accurately predicts ulcerative colitis (UC) severity from endoscopy videos. This deep learning approach could streamline clinical trials and improve patient care by automating endoscopic scoring.

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Computer VisionEfficacy End PointsEndoscopic ScoresMachine Learning

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Area of Science:

  • Gastroenterology
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Endoscopic disease activity scoring is crucial for ulcerative colitis (UC) management but is infrequently performed in clinical practice.
  • Current methods for endoscopic scoring in clinical trials are slow, expensive, and require expert human readers.
  • Automating this process with machine learning holds potential for enhancing clinical care and research.

Purpose of the Study:

  • To develop and validate a machine learning algorithm for automated endoscopic scoring of ulcerative colitis (UC) severity.
  • To assess the algorithm's performance in predicting established endoscopic severity scores using full-length endoscopy videos.

Main Methods:

  • A deep learning model, combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs), was trained on 795 full-length endoscopy videos from a multinational clinical trial.
  • The model extracted features from video frames and predicted the endoscopic Mayo score (eMS) and Ulcerative Colitis Endoscopic Index of Severity (UCEIS).
  • Performance was evaluated by comparing machine-generated scores against expert central reader scores using quadratic weighted kappa (QWK).

Main Results:

  • The machine learning algorithm achieved excellent agreement with expert human readers.
  • The model demonstrated a QWK of 0.844 for eMS and 0.855 for UCEIS.
  • These performance metrics met or exceeded previously published results for UC severity scoring.

Conclusions:

  • A deep learning algorithm can effectively predict UC severity from full-length endoscopy videos.
  • The use of prospective, multinational clinical trial data and video analysis represents an advancement over prior studies.
  • This automated approach shows promise for improving the efficiency and accuracy of endoscopic assessment in UC research and practice.