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相关概念视频

Endoscopic Procedures III: Video Capsule Endoscopy01:28

Endoscopic Procedures III: Video Capsule Endoscopy

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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

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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相关实验视频

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在使用人工智能算法减少后,读取小肠囊内镜.

Dong Jun Oh1, Youngbae Hwang2, Sang Hoon Kim3

  • 1Department of Internal Medicine, Dongguk University Ilsan Hospital, Dongguk University College of Medicine, 27 Dongguk-ro, Ilsandong-gu, Goyang, 10326, Republic of Korea.

BMC gastroenterology
|February 22, 2024
PubMed
概括

人工智能 (AI) 有效地减少了小肠囊内镜 (SBCE) 中的可视化不良图像,显著减少了阅读时间,而不会影响小肠病变的诊断准确性.

关键词:
人工智能的人工智能是人工智能.囊内镜检查 囊内镜检查减少框架的框架缩小粘膜可视化 粘膜可视化

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Automated Measurement of Cryptococcal Species Polysaccharide Capsule and Cell Body
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科学领域:

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

背景情况:

  • 小肠囊内镜 (SBCE) 中可视化的图像不好,使病变的解释变得复杂,并增加了医生的工作量.
  • 一个人工智能 (AI) 算法已被开发来评估SBCE中的粘膜可视化质量.

研究的目的:

  • 评估使用人工智能算法删除可视化不良图像是否会影响SBCE的诊断准确性.
  • 评估人工智能辅助的缩对SBCE阅读时间的影响.

主要方法:

  • 分析了90例SBCE病例,两名内镜医生进行读数.
  • 读数与 (人工智能用户组) 和没有 (人工智能非用户组) 进行了人工智能驱动的清除可视化不良图像 (<50%的粘膜可视化).
  • 在两组之间比较了诊断一致性和阅读时间.

主要成果:

  • 在人工智能用户和非用户组之间观察到统计学上显著的诊断一致性 (k=0.954).
  • 人工智能用户组的平均损伤图像为1401.7,而非人工智能用户组的平均损伤图像为3008.5.
  • 在AI用户组 (77.9分钟) 的阅读时间比AI非用户组 (120.9分钟) 减少了35.6%.

结论:

  • 在SBCE中人工智能辅助减少可视化不良的,不会对最终诊断产生负面影响.
  • 整合人工智能用于缩和可视化评估可以增强人工智能辅助的SBCE解释.