CCRA:基于结肠镜视频分析的结肠清洁度评级算法
Yu Bo1, Shao Wei1, Yao Dengju1
1School of Computer Science and Technology, Harbin University of Science and Technology, Harbin 150080, China.
Heliyon
|November 30, 2023
概括
一个新的结肠清洁度评分算法 (CCRA) 使用人工智能驱动的结肠镜图像分析,提供一致和准确的肠道准备质量评估,消除手动检查的变化.
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:
- 在结肠镜检查期间手动评估结肠清洁性是主观的,容易产生不一致性.
- 准确的肠道准备质量对于有效的结肠镜检测和多检测至关重要.
研究的目的:
- 开发一个自动化结肠清洁度评分算法 (CCRA),用于客观地评估结肠清洁度.
- 为了解决与手动肠道准备质量评估相关的观察者间变异性.
主要方法:
- 从结肠镜视频中截取图像进行分析.
- 使用U-Net在结肠镜图像中对便区域进行语义细分.
- 通过将分段便面积与标准比例进行比较来计算结肠清洁度.
主要成果:
- 该U-Net模型实现了高性能,97.02%的像素精度和90.95%的F1-Score.
- 在评价结肠清洁度方面,CCRA的准确度在92.45%至99.275%之间.
- 该算法提供了快速而精确的结肠清洁度评级.
结论:
- 拟议的CCRA为结肠清洁性评估提供了一个可靠的,自动化的解决方案.
- 这种人工智能驱动的方法提高了评估肠道准备质量的客观性和效率.
- CCRA消除了手动检查的需要,提高了诊断的一致性.
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