面具R-CNN基于多类细分模型用于使用视频喉镜进行内管道输入,使用视频喉镜
Seung Jae Choi1, Dae Kon Kim2,3,4, Byeong Soo Kim5
1Transdisciplinary Department of Medicine and Advanced Technology, Seoul National University Hospital, Seoul, Republic of Korea.
Digital health
|November 29, 2023
概括
这项研究开发了一种人工智能模型,通过视频喉镜图像在内管道输入过程中对口腔结构进行细分. 人工智能显示出潜在的帮助临床医生在紧急气道管理.
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:
- 内管道输入管 (ETI) 对于紧急气道管理至关重要.
- 在ETI期间分析口内结构的AI应用有限.
- 视频喉腔镜 (VL) 在ETI期间提供视觉指导.
研究的目的:
- 开发一种人工智能 (AI) 模型,用于对口腔结构进行细分.
- 用视频喉镜 (VL) 图像来评估AI模型的性能.
- 评估人工智能的实用性,以协助新出现的内内管道输入.
主要方法:
- 临床医生在54个VL视频中手动标记了解剖结构 (舌头,上衣,声带,角膜软骨).
- 测试了三种人工智能模型:使用DeepLabv3+的EfficientNet-B5,使用U-Net的EfficientNet-B5,以及使用Mask R-CNN.
- 细分性能使用子相似系数 (DSC),准确性,回忆,特异性和F1分数来衡量.
主要成果:
- 带有U-Net模型的EfficientNet-B5实现了对表皮 (0.7581),声带 (0.7395),角质软骨 (0.6906) 的高DSC.
- 面具R-CNN模型以每秒32的速度展示了最快的处理速度.
- 模型性能在结构和算法之间有所不同,其中一些实现了临床相关的细分.
结论:
- 开发的人工智能算法可以帮助医疗保健提供者在紧急内内输管过程中.
- 用人工智能驱动的口内结构的细分显示出改善ETI程序的希望.
- 进一步的研究可以优化人工智能模型,以便在呼吸道管理中实时临床应用.
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