使用深度学习用于用视频喉腔镜检测新生儿输内管道的喉孔检测
Abrar Majeedi1, Patrick J Peebles2, Yin Li1,3
1Department of Biostatistics and Medical Informatics, School of Medicine and Public Health, University of Wisconsin-Madison, Madison, WI, USA.
概括
开发了一种人工智能 (AI) 工具,可以在新生儿视频喉镜 (VL) 过程中自动检测喉口. 这种人工智能显示出比平均供应商更快的检测速度,可能会提高输管安全性.
科学领域:
- 医学技术 医学技术 医学技术
- 医学中的人工智能.
- 新生儿护理 新生儿护理
背景情况:
- 视频喉腔镜 (VL) 对于新生儿输管术至关重要.
- 精确的喉腔开口可视化是成功输管的关键.
- 目前的方法可能受到用户体验和实时反的限制.
研究的目的:
- 开发一种人工智能方法,在新生儿VL中自动检测喉开口.
- 为了提高VL用于输管的可用性和有效性.
- 为未来关于改善输管治疗结果的研究奠定基础.
主要方法:
- 一个深度学习模型 (YOLOv8) 在来自84次新生儿输管治疗的1623个视频上受过训练.
- 模型的性能使用14倍交叉验证 (精度,回忆) 进行了评估.
- 人工智能性能与具有不同经验水平的25名医疗提供者进行了比较.
主要成果:
- 人工智能实现了80.8%的精度和75.3%的回忆力,用于检测喉开口.
- 人工智能检测到喉开口比平均医疗提供者快0.31秒.
- 人工智能性能与初学者和中级提供商相当,比专家稍慢一些.
结论:
- 由人工智能驱动的工具可以通过提供实时指导来增强VL.
- 这项技术有可能提高新生儿输管安全性和效率.
- 对缺乏经验的医疗服务提供者来说,人工智能协助可能特别有益.
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