在头癌患者中使用视频光镜吞研究进行自动化消化障碍的表征
Lucía Cubero1, Christophe Tessier2, Joël Castelli3
1Université Rennes, CLCC Eugène Marquis, Inserm, LTSI - UMR 1099, F-35000, Rennes, France; Departamento de Bioingeniería, Universidad Carlos III de Madrid, Madrid, Spain.
Computers in biology and medicine
|February 6, 2025
概括
本研究提出了一个自动化框架,用于分析视频光镜吞研究 (VFSS),以检测头癌 (HNC) 患者的消化不良. 该系统准确地识别关键区域,并对吞障碍进行分类,提供更快,更客观的诊断工具.
科学领域:
- 医学成像分析 医学成像分析
- 医疗保健中的人工智能
- 吞障碍 研究研究 吞障碍
背景情况:
- 缺食症是头癌 (HNC) 放射治疗 (RT) 的常见,令人衰弱的副作用.
- 目前的视频光学吞研究 (VFSS) 的评估是手动的,主观的和时间密集的.
- 需要客观的,自动化的方法来评估缺食症.
研究的目的:
- 开发和验证用于VFSS分析的新型自动化框架.
- 通过深度学习和机器学习来对HNC患者的消化障碍进行表征.
- 为了提高功能障碍诊断的效率和客观性.
主要方法:
- 开发了一个深度学习框架,用于对VFSS感兴趣的十个区域进行代标记.
- 提取了23个动态吞参数,并在患者队列中进行了比较.
- 机器学习分类器被用来将参数分类为四种与失调相关的障碍.
主要成果:
- 标签框架在一个独立的测试集上实现了高精度 (1.6像素平均误差).
- 在健康,HNC和其他消化不良群体之间观察到吞动态的显著差异.
- 机器学习分类器在缺食障碍方面表现出0.60到0.87之间的准确性.
结论:
- 该自动化框架显示了识别关键解剖区域和从VFSS. dysphagia损伤的显著潜力.
- 这项研究为改进的,自动化的消化障碍分析奠定了基础,特别是对于HNC放射治疗患者.
- 需要进一步的研究来完善系统,考虑到数据集大小和VFSS的变化.
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