在B模式超声波中使用纹理分析与机器学习分类器的组合来识别肌肌关节触发点
Fatemeh Shomal Zadeh1,2, Ryan G L Koh2, Banu Dilek3
1Institute of Biomedical Engineering, University of Toronto, Toronto, ON M5S 3G9, Canada.
Sensors (Basel, Switzerland)
|December 23, 2023
概括
定量超声波 (US) 技术显示出识别肌肉关触发点 (MTrPs) 的潜力. 虽然传统分析发现了显著的结果,但机器学习模型难以在MTrP和健康肌肉之间进行强有力的区分.
科学领域:
- 肌肉骨超声波 肌肉骨超声波
- 疼痛医学 医学 疼痛医学
- 医学成像分析 医学成像分析
背景情况:
- 肌肌关节疼痛综合征 (MPS) 是一种慢性疼痛状况.
- 肌肌触发点 (MTrPs) 是MPS的特征.
- 定量超声波 (美国) 提供了MTrP检测的潜力.
研究的目的:
- 评估基于纹理的美国方法来区分活跃的MTrPs,潜伏的MTrPs和健康的肌肉.
- 为了比较传统的统计分析和机器学习 (ML) 在这种歧视任务中的有效性.
主要方法:
- 从63名参与者中收集了90张B模式US图像的上梯形肌肉.
- 应用了四种纹理特征方法来识别MTrPs.
- 利用机器学习和单向差异分析进行歧视分析.
主要成果:
- 传统的统计分析揭示了大多数检查的特征的统计学上显著的结果.
- 机器学习技术表明,在实现群体之间强有力的歧视方面存在挑战.
- ML确定了相关性和平均值作为分类三个肌肉状态的关键特征.
结论:
- 传统的统计分析和ML之间存在差异,在使用美国纹理特征对MTrP进行歧视时存在差异.
- 需要进一步的研究来完善基于纹理的美国方法,以准确识别MTrP.
- 美国的纹理分析对诊断肌筋关节疼痛综合征具有前景.
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