在基于转移学习和临床实践的超声波成像中识别托菲
Tzu-Min Lin1,2, Hsiang-Yen Lee2, Ching-Kuei Chang2
1Division of Allergy, Immunology and Rheumatology, Department of Internal Medicine, School of Medicine, College of Medicine, Taipei Medical University, Taipei, Taiwan.
Scientific reports
|August 2, 2023
概括
这项研究表明,深层卷积神经网络可以在超声波图像中准确地识别托菲. 这种人工智能方法为诊断常见的代谢障碍 - - 痛风提供了一个有前途的工具.
科学领域:
- 人工智能在医学中的应用
- 医学成像分析 医学成像分析
- 类风湿病和代谢障碍 类风湿病和代谢障碍
背景情况:
- 痛风是一种代谢障碍,导致结晶沉积 (tophi),导致严重的关节炎.
- 目前的痛风诊断依赖于临床,实验室和成像发现,成像得分高达4分.
- 现有的成像方法,如双能CT,是昂贵的,涉及辐射,而超声波需要专家解释.
研究的目的:
- 开发和评估深度卷积神经网络 (CNN) 模型,用于在超声波图像中自动识别托菲.
- 与传统方法相比,评估CNN模型在检测痛风相关的托菲的准确性和可靠性.
主要方法:
- 转移学习被应用于训练CNN模型,包括InceptionV3,ResNet101和VGG19.
- 这些模型被训练在肌肉骨超声波图像中识别标志着托菲的模式.
- 对每个模型的性能进行了评估,使用准确度,灵敏度和精度指标.
主要成果:
- VGG19模型获得了最高的精度 (0.918 ± 0.020),其次是ResNet101 (0.913 ± 0.015).
- 此外,VGG19显示了最高的灵敏度 (0.747 ± 0.056),而ResNet101显示了最高的精度 (0.863 ± 0.098).
- 该研究证实了使用重新训练的CNN用于超声波中准确检测托菲的可行性.
结论:
- 深层卷积神经网络可以有效地重新训练,以在超声波图像中高精度地识别托菲.
- 人工智能驱动的超声波分析提供了一种潜在的非侵入性,成本效益高,可靠的痛风诊断方法.
- 这项技术可以提高痛风患者的诊断效率和可访问性.
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