一个基于实时深度学习的系统,用于通过白光内镜估计结直肠多体大小:开发和多中心前性验证
Jing Wang1,2,3,4, Ying Li5, Boru Chen1,2,3,4
1Department of Gastroenterology, Renmin Hospital of Wuhan University, Wuhan, China.
Endoscopy
|October 12, 2023
概括
一个新的AI系统,ENDOANGEL-CPS,可以准确地实时估计结肠直肠息肉的大小. 这种深度学习工具可以提高聚测量准确度,并有可能优化监测策略.
科学领域:
- 胃肠病学 胃肠病学
- 人工智能的人工智能
- 医疗成像医学成像
背景情况:
- 结肠直肠多体的大小对于确定多体术装置和监测间隔至关重要.
- 需要准确的实时聚大小估计来指导临床决策.
研究的目的:
- 开发和评估一个基于深度学习的系统 (ENDOANGEL-CPS) 来实时估计结直肠聚的大小.
- 为了比较系统的性能与内镜师在尺寸测量和监测建议中的表现.
主要方法:
- ENDOANGEL-CPS系统通过计算从内镜镜片的距离来估计多的尺寸.
- 深度估计器网络在虚拟结肠视频上进行了训练,并在模拟和真实世界的临床数据上进行了测试.
- 通过将系统估计与地面真相和内镜评估进行比较来评估性能.
主要成果:
- ENDOANGEL-CPS在深度估计中表现出高精度 (11.3%的相对误差) 和与地面真相的强烈一致 (CCCs为0.89-0.93).
- 该系统在断片大小估计准确度 (89.9%对54.7%) 和一致性 (0.89对0.41) 中明显优于内镜师.
- 与内镜医生相比,ENDOANGEL-CPS在不适当的监测建议中显示出较低的错误率 (1.5%与16.6%相比).
结论:
- ENDOANGEL-CPS显示出显著的潜力,可以提高结直肠多体大小测量的准确性.
- 该系统可以提高基于大小的监测间隔建议的精度.
- 像ENDOANGEL-CPS这样的人工智能驱动的工具可以优化结直肠癌查和管理.
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