一种公共卫生方法,通过面部表情分析在胸痛患者中自动识别疼痛强度,以优先考虑紧急护理
Rita Wiryasaputra1,2, Yu-Tse Tsan3,4,5,6, Qi-Xiang Zhang4,7
1Department of Industrial Engineering and Enterprise Information, Tunghai University, Taichung 407224, Taiwan.
Diagnostics (Basel, Switzerland)
|October 29, 2025
概括
自动化面部表情分析可以帮助评估胸痛强度,帮助医疗保健专业人员. 这种使用深度学习的AI工具提供了一种非侵入性的方法来监测患者的不适,而无需诊断心肌梗塞.
科学领域:
- 人工智能在医学中的应用
- 计算机视觉用于医疗保健
- 疼痛管理技术 疼痛管理技术
背景情况:
- 心血管疾病是导致死亡的主要原因,胸痛是紧急诊所常见的症状.
- 胸痛的严重程度并不总是与心肌梗塞的严重程度相关.
- 面部表情传达疼痛的强度,特别是在非语言患者.
研究的目的:
- 开发一种用于识别面部疼痛表情的自动化系统,以监测胸痛强度.
- 评估深度学习模型,特别是YOLO系列,用于疼痛评估中的计算效率和诊断准确性.
- 为医疗保健专业人员创建一个辅助工具,以观察患者的不适.
主要方法:
- 使用流媒体技术捕捉实时面部表情.
- 深度学习框架包括PSPI分数和YOLO模型用于疼痛水平分类.
- 对YOLO系列模型进行比较分析,重点关注效率和准确性.
主要成果:
- 一个定制的YOLOv4模型在最快的训练时间内实现了97%的疼痛水平识别精度.
- 该系统具有基于Web的界面,配有彩色编码的疼痛指标,可灵活部署.
- 该模型在评估计算效率和诊断准确性方面表现出卓越的性能.
结论:
- 自动化面部疼痛评估显示出作为医疗保健专业人员的辅助工具的潜力.
- 该系统监测患者的不适,但不能诊断心肌梗塞的原因.
- 未来的工作包括整合临床元数据和边缘计算,以便在不同环境中实时监控.
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