使用深度学习方法,对超声波引导的关节上阻塞进行最佳视图检测
Yumin Jo1, Dongheon Lee2,3, Donghyeon Baek4
1Department of Anaesthesiology and Pain Medicine, College of Medicine, Chungnam National University and Hospital, 282 Munhwar-ro, Jung-gu, Daejeon, 35015, Republic of Korea.
Scientific reports
|October 11, 2023
概括
这项研究开发了一种使用卷积神经网络 (CNN) 的计算机辅助诊断 (CADx) 系统,以确定超声波引导的关节上阻塞 (SCB) 的最佳视图. 在实时识别方面,CADx系统表现出高性能,帮助临床医生实现完整的SCB.
科学领域:
- 医疗成像医学成像
- 人工智能在医学中的应用
- 区域麻醉地区麻醉
背景情况:
- 成功的超声导向关节上阻塞 (SCB) 取决于精确的声解剖学和最佳视图识别.
- 目前的卷积神经网络 (CNN) 分段方法在确定SCB的最佳视图方面存在局限性.
- 开发实时计算机辅助诊断 (CADx) 系统可以增强程序指导.
研究的目的:
- 开发基于CNN的CADx系统,用于实时识别完整SCB的最佳视图.
- 通过提供实时指导,帮助非专家临床医生执行SCB.
- 在CADx系统中评估分类和细分方法的性能.
主要方法:
- 追溯收集881名患者的超声波视频,用于CADx系统开发 (600名培训/验证,281名测试).
- 开发一个包含分类 (ResNet) 和细分 (U-Net) 方法的CADx系统.
- 使用混矩阵进行性能评估,重点关注准确性,精度,回忆,f1得分和AUROC.
主要成果:
- 使用ResNet34和封闭循环单元的分类方法实现了最高的性能 (精度为0.901,AUROC为0.936).
- 与分类方法相比,细分方法 (U-Net与ResNet34) 的表现较差.
- 开发的CADx系统在确定SCB的最佳视图方面表现出高效率.
结论:
- 开发的CADx系统有效地实时识别了SCB的最佳视图.
- 该系统显示了扩展到其他解剖区域和实时临床应用的潜力.
- 这种人工智能驱动的工具可以帮助临床医生,特别是非专家,执行SCB程序.
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