深度学习用于评估视频喉镜视频喉镜用新生儿输管过程中喉镜插入深度
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
一个新的AI模型可以在新生儿输管治疗期间评估视频喉腔镜 (VL) 叶片深度,帮助喉可视化. 虽然有效的浅和喉区域深度,深插入检测是有限的.
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) 增强了用于新生儿输管治疗的喉视觉化.
- 目前无法在VL期间为叶片插入深度提供实时指导.
- 准确的深度评估对于安全有效的新生儿输管术至关重要.
研究的目的:
- 开发和评估一种深度学习模型,用于在新生儿视频喉腔镜中对叶片插入深度进行分类.
- 评估模型在区分浅,喉区域和深入插入深度方面的表现.
- 收集临床医生对人工智能辅助VL设备反方式的偏好.
主要方法:
- 一个深度学习模型在来自132个新生儿VL视频的298,955个注释上接受了训练.
- 该模型在两个NICU的单一平台上进行了五次交叉验证.
- 一项对31名临床医生的调查评估了首选的反方式 (视觉,语音,触觉).
主要成果:
- 该模型在喉区域 (0.894) 和浅插入 (0.718) 中获得了高F1分.
- 由于其稀有 (2.7%),深插入的性能很低 (F1=0.034).
- 临床医生更喜欢视觉,最小的反,而不是听觉或触觉警报.
结论:
- 支持人工智能的VL显示了改善刀片插入深度评估和培训的潜力.
- 由于数据有限,该模型对深插入的有效性需要进一步调查.
- 未来的多站点研究是必要的,以将AI整合到临床实践中并评估患者的结果.
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