在可变长度的视频光学吞研究中检测气道入侵:一种视觉转换器方法用于口腔喉乱症
Hesam Abdolmotalleby1, Joseph M Reinhardt1, Douglas J Van Daele2
1Roy J. Carver Department of Biomedical Engineering, University of Iowa, Iowa City, IA 52242, USA.
Diagnostics (Basel, Switzerland)
|December 30, 2025
概括
使用人工智能自动诊断失消症显示出有希望. 视觉转换器 (ViT) 模型准确地分析了视频光镜吞研究 (VFSS),优于传统方法更快地检测吞困难.
科学领域:
- 医学成像分析分析 医学成像分析
- 医疗保健中的人工智能
- 吞障碍的诊断 吞障碍的诊断
背景情况:
- 吞障碍,吞障碍,由于各种原因,包括衰老和神经退行症,显著影响生活质量.
- 视频光镜吞研究 (VFSS) 是诊断失消症的黄金标准,但需要耗时的手动解释.
- 自动化VFSS分析对于高效和成本效益的临床工作流程至关重要.
研究的目的:
- 开发和评估一种自动化系统,使用VFSS对吞异常进行分类.
- 利用先进的深度学习技术,提高诊断准确度,检测失调症.
主要方法:
- 开发了一个视觉变压器 (ViT) 模型,结合了时间滑动窗和3D补丁标记化,用于VFSS的时空分析.
- ViT模型在107个人的1154个VFSS序列上进行了训练和验证.
- 性能与已建立的卷积神经网络 (CNN) 架构进行了比较,包括VGG-16,ResNet-50,EfficientNet和MobileNet.
主要成果:
- ViT模型实现了高诊断性能,包括84.37%的准确性,90.81%的灵敏度和79.49%的特异性.
- 在多个性能指标 (p < 0.05) 上,ViT显著超过了所有测试的CNN基线.
- 曲线下的面积 (AUC) 达到0.878,表明强大的区分能力.
结论:
- 基于注意力的时空建模ViT提供了强大的VFSS的分类.
- 这种人工智能驱动的方法适用于选工作流程,需要快速检测吞异常.
- 开发的ViT模型为可临床部署的自动化VFSS分析系统提供了基础.
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