使用基于视频的深度学习模型对健康和受伤的手进行输液评估
Vineet R Shenoy1, Carly Q Kingston2, Mantej Singh3
1From the Departments of Electrical and Computer Engineering.
Plastic and reconstructive surgery
|June 9, 2025
概括
深度学习视频分析显示,对在受控环境中对手指 perfusion 的分类有前途. 然而,在不受控制的急诊室条件下,性能下降,这表明需要进一步开发临床分拣工具.
科学领域:
- 医学技术 医学技术 医学技术
- 医学中的人工智能
- 创伤护理的护理人员.
背景情况:
- 准确的现场手部创伤评估是困难的,影响患者的治疗结果和医疗保健系统.
- 非接触式,基于视频的深度学习为改善 perfusion 评估和 triage 提供了潜力.
- 目前在创伤环境中评估手指输液的方法有限.
研究的目的:
- 开发和评估深度学习方法,以使用非接触式视频对充血指和缺血指进行分类.
- 评估这些方法在受控和急性创伤环境中的性能.
主要方法:
- 从对照人群 (患有诱导性缺血症) 和急性受伤患者中收集了iPhone视频.
- 深度学习被用来从视频中提取成像光聚光学 (iPPG) 波形.
- 一个ResNet-18分类器分析了iPPG光谱图,以区分 perfused 和缺血状态,与临床基础真相相比较.
主要成果:
- 在受控的设置中,该算法实现了72%的灵敏度,74%的积极预测值和90%的准确性来分类缺血指.
- 在急性创伤环境中,由于不受控制的变量,性能显著降低 (33%的灵敏度,24%的PPV,77%的准确性).
- 这项研究捕捉了48名对照组和15名急性受伤患者的视频.
结论:
- 深度学习 perfusion 分类在受控条件下显示出高精度.
- 作为分类工具的临床适用性受到不受控制的现实创伤环境中的性能退化所限制.
- 需要进一步的研究,以纳入急性创伤特异变量,以提高临床效用.
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