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Introduction of an Integrated Pathology Image Management, Artificial Intelligence, and Reporting System
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Preparation of image databases for artificial intelligence algorithm development in gastrointestinal endoscopy.

Chang Bong Yang1, Sang Hoon Kim1, Yun Jeong Lim1

  • 1Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, Goyang, Korea.

Clinical Endoscopy
|May 31, 2022
PubMed
Summary

Artificial intelligence (AI) enhances medical imaging, particularly in gastrointestinal endoscopy. AI-assisted tools improve real-time decision-making for endoscopists and guide dataset creation for algorithm development.

Keywords:
Artificial intelligenceComputer-aided diagnosisDeep learningGastrointestinal endoscopyLearning dataset

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Gastrointestinal Endoscopy

Background:

  • Deep learning advances have integrated artificial intelligence (AI) into medical imaging.
  • Convolutional neural networks, inspired by the human visual cortex, are key to AI image recognition.
  • AI applications in gastrointestinal endoscopy show significant promise for computer-aided diagnosis.

Purpose of the Study:

  • To review the current state-of-the-art AI in gastrointestinal endoscopy.
  • To provide a practical guide for developing AI algorithms through image dataset creation.

Main Methods:

  • Review of recent advancements in AI and machine learning for medical imaging.
  • Focus on convolutional neural networks and their application in endoscopy.
  • Discussion of practical considerations for building learning image datasets.

Main Results:

  • AI has demonstrated remarkable outcomes in computer-aided diagnosis within gastrointestinal endoscopy.
  • AI-assisted clinical practice is poised to support endoscopists with real-time decision-making.
  • Technological progress has enhanced machine-learning techniques and computational power.

Conclusions:

  • AI implementation in gastrointestinal endoscopy is expected to overcome existing hurdles.
  • AI tools will significantly aid endoscopists, improving diagnostic accuracy and efficiency.
  • Developing robust image datasets is crucial for advancing AI algorithm development in this field.