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现场选中的空中创新:开发无人机集成大规模伤亡选算法 (DIMaCTA)
İsmail Tayfur1,2, Abdülkadir Gündüz2,3, Perihan Şimşek2,4
1Sancaktepe Şehit Prof. Dr. İlhan Varank Education and Research Hospital, Department of Emergency Medicine, University of Health Sciences, Istanbul, Türkiye.
Prehospital emergency care
|January 21, 2026
概括
开发了一种新的无人机辅助分拣算法 (DIMaCTA),以改善大规模伤亡事件的响应. 专家们验证了算法,发现无人机应用对于在难以到达的地区进行持续,节省时间的分类是有效的.
科学领域:
- 紧急医疗 紧急医疗
- 灾害管理 灾害管理
- 医疗保健中的人工智能
背景情况:
- 大规模伤亡事件 (MCIs) 的频率和严重性正在增加.
- 传统的大规模伤亡分拣具有挑战性,需要远程分拣解决方案.
- 无人机技术和图像处理为远程血液动力学监测和分类提供了潜力.
研究的目的:
- 开发无人机综合大规模伤亡选算法 (DIMaCTA).
- 为了解决无人机辅助分类算法有限的研究.
主要方法:
- 采用了两阶段的方法:通过文献审查和现场经验进行初始算法开发,然后通过紧急医疗专家进行修改的Delphi研究.
- 关于算法决策点和评估标准的专家共识是使用内容有效率比率 (CVR) 和内容有效率指数 (CVI) 来评估的.
- 通过问卷收集了参与者关于无人机辅助选的意见.
主要成果:
- 86.7%的参与者认为基于无人机的算法对无法进入的区域的持续和节省时间的分拣有效.
- 在第一个Delphi轮中,在算法参数和决策点上达成了80%以上的专家共识.
- 商定了具体的值:脉冲≤30/分钟,温度≤28°C用于"紧急"/"死亡"分类,脉冲≥100/分钟用于"立即"/"延迟"情况. CVR和CVI值表明内容有效性强.
结论:
- 开发的DIMaCTA是一种使用图像处理的无人机辅助分拣算法,适合作为现场分拣工具.
- 预计DIMaCTA的无人机应用将在MCIs期间加快重症患者的优先级.
- 需要进行进一步的验证和影响评估,以确定算法在大规模事故管理中的作用.
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