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Endoscopic Procedures III: Video Capsule Endoscopy01:28

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Capsule endoscopy, or wireless or video capsule endoscopy, is a diagnostic procedure for examining the entire gastrointestinal tract. Patients swallow a capsule about the size of a vitamin tablet. The capsule is equipped with a transmitter, a battery, an LED light source, and a color video camera to capture images throughout the gastrointestinal tract. This procedure is particularly useful for diagnosing conditions such as Crohn's disease, ulcerative colitis, tumors, polyps, ulcers,...
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Imaging Studies III: Gastrointestinal Motility Studies and Virtual Colonoscopy01:26

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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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Endoscopic Procedures I: Esophagogastroduodenoscopy01:29

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An Esophagogastroduodenoscopy (EGD) is a diagnostic procedure in which an endoscopist uses a flexible, lighted endoscope to visualize the upper gastrointestinal (GI) tract. The procedure includes visualizing the oropharynx, esophagus, stomach, and the first part of the small intestine, the duodenum.
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Sigmoidoscopy and laparoscopy are distinct medical procedures that enable physicians to internally inspect different parts of the GI tract. Although they serve different purposes, each is essential for diagnosing and, in some cases, treating various medical conditions.
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基于视觉解释的可解释的人工智能在消化内镜中的应用

Xiaohan Cai1, Zexin Zhang1, Siqi Zhao1

  • 1Department of Gastroenterology, Tianjin Medical University General Hospital, No. 154, Anshan Road, Heping District, Tianjin 300052, China.

Bioengineering (Basel, Switzerland)
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概括

可解释性AI (XAI) 通过使复杂的"黑子"模型可解释,提高了对消化内镜人工智能 (AI) 的信任. 这篇评论探讨了XAI的情况.

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深度学习是一种深度学习.消化内镜检查是消化内镜检查.可解释的人工智能机器学习是机器学习.医学成像医学成像视觉解释 视觉解释

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科学领域:

  • 医疗成像医学成像
  • 人工智能的人工智能
  • 胃肠病学 胃肠病学

背景情况:

  • 人工智能 (AI) 在分析消化内镜图像以诊断胃肠道疾病方面表现有前途.
  • 深度学习模型虽然强大,但经常充当"黑子",由于缺乏可解释性,妨碍了临床信任和采用.
  • 可解释人工智能 (XAI) 对于提高透明度和建立对人工智能驱动的医疗决策的信任至关重要.

研究的目的:

  • 系统地审查可解释AI (XAI) 在消化内镜图像分析中的研究进展和应用.
  • 专注于基于视觉解释的XAI方法及其在提高透明度方面的作用.
  • 确定XAI在临床实践中实施的当前障碍和未来方向.

主要方法:

  • 对34篇文章的系统综述,涉及各种消化内镜方式 (食管胃管内镜,结肠镜,内镜超声镜,无线囊内镜).
  • 澄清XAI定义和主流分类.
  • 介绍基于视觉解释的XAI方法的原理和特征.
  • 对XAI在消化内镜图像分析中的应用进行审查.

主要成果:

  • 越来越多地应用XAI方法,特别是视觉解释技术,以提高消化内镜中AI模型的可解释性.
  • 该综述综合了当前的研究,突出了XAI在不同内镜手术中的理解和应用方面的进展.
  • 确定了消化内镜中可靠的人工智能的挑战和未来研究途径.

结论:

  • 在消化内镜中,XAI为开发透明和可靠的AI辅助诊断和治疗系统提供了理论基础.
  • 促进XAI的实施和应用对于在胃肠病学中更广泛地采用AI的临床应用至关重要.
  • 通过XAI增强的可解释性促进了内镜图像分析中的关键人机协作.