从使用视觉转换器的面部表情视频中识别急性疼痛
概括
使用视频视觉转换器 (ViViT) 进行自动疼痛检测,对有沟通障碍的患者来说是有前途的. 这种人工智能方法准确地估计了面部表情的疼痛程度,有助于临床评估.
科学领域:
- 计算机科学 计算机科学
- 生物医学工程 生物医学工程
- 人工智能的人工智能
背景情况:
- 准确的疼痛评估对于患者的诊断和治疗至关重要.
- 通过面部表情自动检测疼痛,帮助有沟通障碍的患者.
研究的目的:
- 开发和评估视频视觉转换器 (ViViT) 用于自动疼痛识别.
- 为了捕获面部的时空空间信息,用于二进制疼痛分类.
主要方法:
- 在两个急性疼痛数据集上训练和评估ViViT模型:AI4PAIN挑战和BioVid疼痛.
- 与基线模型相比,ViViT性能进行比较:ResNet50和ResNet50+3DCNN.
主要成果:
- 在AI4PAIN数据集上,ViViT实现了66.96%的准确性.
- 在BioVid数据集上,ViViT实现了79.95%的准确性,表现优于基线模型.
结论:
- 拟议的ViViT模型在面部表情的自动疼痛检测方面表现出卓越的性能.
- 这项技术为临床环境中的客观疼痛估计提供了宝贵的见解.
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