基于卷积神经网络的人工智能辅助多任务结肠镜戒断质量控制系统的开发 (带视频)
Jian Chen1,2, Menglin Zhu1, Zhijia Shen3
1Department of Gastroenterology, Changshu Hospital Affiliated to Soochow University, Suzhou, Jiangsu, China.
Frontiers in physiology
|October 27, 2025
概括
本研究介绍了EWT-SpeedNet,这是一种用于结肠镜检查质量控制的AI系统. 它准确地监测了吸收速度,总吸收时间和有效吸收时间,提高了结直肠癌查.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 胃肠病学 胃肠病学
背景情况:
- 结肠镜检查对于结肠直肠癌查和诊断至关重要.
- 退出阶段对于粘膜检查和病变检测至关重要.
- 目前的方法缺乏用于退出阶段的标准化质量控制.
研究的目的:
- 开发一种人工智能系统,用于在结肠镜检查中进行多任务戒断质量控制.
- 为了监控抽出速度,总抽出时间和有效抽出时间.
- 提高粘膜检查和病变检测率的充分性.
主要方法:
- 利用卷积神经网络 (YOLOv11) 在结肠镜数据上进行转移学习.
- 带注释的图像/视频用于乳房内输管,仪器操作和正常的粘膜.
- 集成的拉普拉斯运算符用于模糊检测和感知哈希算法用于速度监控.
主要成果:
- 在测试组中,YOLOv11 m获得了96.58%的加权平均精度和0.9975 AUC.
- 人工智能系统在测量有效禁用时间方面与专家内镜师保持高度一致 (ICC = 0.969).
- EWT-SpeedNet提供实时速度可视化和提款时间的自动计算.
结论:
- 开发的EWT-SpeedNet系统有效地支持标准化和高效的结肠镜撤回监测.
- 人工智能驱动的质量控制可以提高结肠镜手术的可靠性.
- 该系统有可能通过优化结肠镜质量来提高结肠直肠癌检测率.
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