在老年患有关节骨折的患者中,基于多式联络信息融合的自动疼痛分类
Shuang Yang1, Wen Luo2,3, Tao Yang3,4
1The 2nd Ward of Hip Joint Surgery, Tianjin Hospital, Tianjin, China.
Scientific reports
|July 2, 2025
概括
这项研究为老年关节骨折患者开发了一种多式疼痛识别系统,集成了面部表情和音频数据. 该系统实现了85%的准确性,证明了有效的现实世界疼痛评估.
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
- 老年医学 老年医学
背景情况:
- 单模式疼痛识别方法在准确评估疼痛方面存在局限性,特别是在脆弱人群中,如部骨折的老年人.
- 客观的疼痛测量对于有效的疼痛管理和患者的治疗结果至关重要.
- 现有的系统往往无法捕捉到疼痛表达的复杂,多方面的性质.
研究的目的:
- 开发和验证一种新的多式联络疼痛识别系统,用于患有关节骨折的老年患者.
- 整合面部表情和音频数据,以实现更强大的疼痛检测.
- 通过先进的AI技术,提高临床环境中疼痛评估的准确性.
主要方法:
- 使用ResNet-50进行面部表情分析和VGGish网络进行基于音频的疼痛识别.
- 整合了一种道注意力机制,以改进特征表示和改善疼痛水平歧视.
- 采用加权总和融合策略,将单模输出结合到统一的多模模型中.
- 在一个自建的关节骨折疼痛数据集 (80:20分) 上训练并验证了模型,并在BioVid热痛数据库上进行了测试.
主要成果:
- 优化了LSTM和道注意力的VGGish模型,在500次代后,在部骨折疼痛数据集上实现了80%的准确性.
- 在BioVid热痛数据库上的测试显示,疼痛等级4的疼痛准确率为85%.
- 该系统成功地集成了面部和语音数据,用于疼痛识别.
结论:
- 开发的多式疼痛识别系统是第一个经过临床验证的方法,集成了关节骨折患者的面部表情和语音数据.
- 这些发现证实了多式联络信息融合的可行性和有效性,用于在现实临床环境中准确评估疼痛.
- 这种由人工智能驱动的系统为改善老年护理中的疼痛管理提供了有希望的工具.
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