开发一种基于人工智能的图像识别系统,用于分析气管管道输入的时间序列分析
Yu-Hwa Wu1, Kun-Yi Huang2, Alex Chia-Chih Tseng3
1From the Department of Anesthesia, National Cheng Kung University Hospital, College of Medicine, National Cheng Kung University, Tainan, Taiwan.
Anesthesia and analgesia
|February 21, 2024
概括
这项研究引入了一个使用YOLOv3的AI系统来分析视频喉镜图像用于气管管道输液. 人工智能精确地划分了输管阶段,使得总输管时间的测量得以一致.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 麻醉学 麻醉学
背景情况:
- 输入管道总时间 (TIT) 是气管管道输入管道 (TI) 困难的一个关键指标.
- 定义TIT开始/结束点的变化阻碍了研究比较.
- 视频喉镜 (VLS) 提供可视化数据,用于客观的TI分析.
研究的目的:
- 开发和验证人工智能驱动的时间序列系统,用于在TI期间分析VLS图像.
- 建立TI阶段和整体输管时间的一致和客观测量.
- 为了克服当前TIT测量方法中的不一致性.
主要方法:
- 使用#3 Macintosh VLS开发了一个时间序列系统.
- 在887张图像上训练了一种AI模型 (YOLOv3),以识别气道结构和切割点.
- 该系统与专家识别的切割点和30个易于TI的时间序列数据进行了验证.
主要成果:
- 人工智能模型实现了切割点的高识别率 (精炼后99.4%).
- 人工智能计算的阶段持续时间和TIT与专家评估有很强的相关性 (r > 0.914对于阶段,r = 0.99对于TIT).
- 当与研究生医生数据 (r > 0.95) 进行验证时,该系统表现出高准确性.
结论:
- YOLOv3对于分析TI中的VLS图像是有效的.
- 通过人工智能驱动的气道结构检测,可以为TIT进行一致的开始/结束点选择.
- 对VLS图像的时间序列分析对于未来的TI研究至关重要.
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