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基于深度学习模型,自动检测具有不同出血风险的小肠病变.

Rui-Ya Zhang1, Peng-Peng Qiang2, Ling-Jun Cai1

  • 1Department of Gastroenterology, The Fifth Clinical Medical College of Shanxi Medical University, Taiyuan 030012, Shanxi Province, China.

World journal of gastroenterology
|February 5, 2024
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概括

这项研究引入了一种用于小肠囊内镜的深度学习模型,可以准确识别病变和出血风险,显著提高诊断效率和医生检测高风险出血病例的准确性.

关键词:
人工智能的人工智能是人工智能.出血的风险 发生出血的风险囊内镜检查 囊内镜检查深度学习是一种深度学习.图像的分类图像的分类.对象检测检测对象检测对象检测

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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:

  • 深度学习为小肠 (SB) 囊内镜 (CE) 提供高效的自动图像识别.
  • 现有的深度学习模型在准确诊断SB病变和评估出血风险方面面临挑战.
  • 医生诊断效率可以通过先进的AI工具来提高.

研究的目的:

  • 开发一种新的深度学习模型,用于从CE图像中分类和检测SB病变.
  • 准确评估与已识别的SB病变相关的出血风险.
  • 提高诊断效率和识别高风险出血患者.

主要方法:

  • 采用了两阶段的深度学习方法,将图像分类和对象检测结合起来.
  • 一个改进的ResNet-50模型对图像进行了分类 (损伤,正常,无效).
  • 一个改进的YOLO-V5模型检测了病变类型,出血风险和位置,与人类内镜师相比,性能更好.

主要成果:

  • 该模型实现了98.96%的精度,性能优于单模组系统.
  • 模型辅助阅读显示高灵敏度 (99.17%),特异性 (99.92%) 和准确性 (99.86%).
  • 该模型处理图像的速度明显快 (48 ms/图像) 比医生 (0.40 ± 0.24 秒/图像).

结论:

  • 一个用于分类和检测的综合深度学习模型有效地诊断了CE中的SB病变和出血风险.
  • 这种方法提高了医生诊断效率和识别高风险出血组的能力.
  • 该模型显示了改善胃肠病学患者护理的巨大潜力.