在BioVid热痛数据库上的机器学习方法用于疼痛强度估计
Melpo Pittara1, Andreas Anastasiou2,3, Konstantinos Andreou2,3
1Computer Science, and Artificial Intelligence, Bernoulli Institute of Mathematics, University of Groningen, Groningen, Netherlands. melpopittara@gmail.com.
Scientific reports
|November 1, 2025
概括
这项研究使用机器学习对面部表情进行了客观的疼痛评估. 卷积神经网络 (CNN) 在检测疼痛强度方面显示出最高的准确性,为非沟通患者提供了潜力.
科学领域:
- 生物医学工程 生物医学工程
- 医疗保健中的人工智能
- 疼痛管理研究 疼痛管理研究
背景情况:
- 传统的疼痛量表 (NRS,VAS,VRS) 对某些患者群体来说是主观的,不可靠的.
- 客观的疼痛评估对于准确的治疗和改善患者生活质量至关重要.
- 机器学习为开发客观疼痛测量工具提供了一个有希望的途径.
研究的目的:
- 通过机器学习技术引入和评估客观的疼痛测量模型.
- 为了比较卷积神经网络 (CNN),VGG16,卷积视觉变换器 (CvT) 和MobileViT在从面部表情分类疼痛强度方面的表现.
- 评估自动化疼痛评估在临床应用中的潜力,特别是对于非沟通患者.
主要方法:
- 利用了来自BioVid热痛数据库的面部图像,捕捉了峰值疼痛和没有疼痛的时刻.
- 训练和评估了四种机器学习模型:CNN,VGG16,CvT和MobileViT.
- 基于对疼痛强度分类准确性的模型性能进行比较.
主要成果:
- 在分类疼痛强度方面,CNN模型实现了最高的平均准确率 (0.71).
- CvT模型表现出强的性能,平均准确率为0.69.
- VGG16 (0.56) 和MobileViT50 (0.60) 的准确性明显较低,这表明从面部图像分类疼痛存在挑战.
结论:
- 机器学习模型,特别是CNN,显示出客观和一致的疼痛评估的巨大潜力.
- 自动化面部表情分析可以帮助评估非语言或认知障碍患者的疼痛.
- 未来的研究应该集中在多式联运数据集成上,以提高疼痛检测系统的稳定性.
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