自动化面部疼痛评估使用双重注意力CNN与临床校准的高可靠性和可重复性框架
Albert Patrick Sankoh1, Ali Raza2, Khadija Parwez3
1Khoury College of Computer Sciences, Northeastern University, Boston, MA 02115, USA.
Biomimetics (Basel, Switzerland)
|January 27, 2026
概括
本研究介绍了一种使用双重注意力卷积神经网络 (CNN) 的自动面部疼痛评估系统. 人工智能模型准确地检测出面部表情的疼痛水平,为客观疼痛测量提供了可靠的解决方案.
科学领域:
- 生物医学工程 生物医学工程
- 人工智能的人工智能
- 疼痛医学 医学 疼痛医学
背景情况:
- 准确的疼痛评估具有挑战性,特别是对于非语言患者,因为当前的方法依赖于主观的自我报告或临床医生的观察.
- 现有的疼痛评估工具缺乏客观性和一致性,阻碍了有效的临床管理和研究.
研究的目的:
- 开发和验证使用双重注意力卷积神经网络 (CNN) 的新型自动化面部疼痛评估框架.
- 为了从面部表情中实现临床校准,高可靠性和可解释的疼痛水平检测.
- 为临床环境中疼痛监测提供一个客观和一致的工具.
主要方法:
- 开发了一种双重注意力CNN架构,结合了多头空间注意力和一个增强的频道注意力区块.
- 采用标签光滑 (α = 0.1) 和AdamW优化,以实现稳定的模型融合.
- 评估了临床注释数据集的框架,使用五个疼痛类别的学科分层抽样.
主要成果:
- 获得了90.19%±0.94%的测试准确率和83.60%±1.55%的5倍交叉验证准确率.
- 获得了0.90的F1得分,科恩的 κ = 0.876,以及0.991 (宏观) 和0.992 (微观) 的AUC.
- 格拉德-CAM可视化证实了该模型专注于生理上相关的面部区域.
结论:
- 拟议的框架为自动化面部疼痛评估提供了一种强大,可解释和可重复的方法.
- 这种由人工智能驱动的系统展示了与现实世界自动化疼痛监测系统集成的巨大潜力.
- 仿生方法与自然疼痛感知机制保持一致,增强客观疼痛评估.
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