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Measuring Local Tissue Strains in Tendons via Open-Source Digital Image Correlation
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使用机器学习支持的纹理分析来区分早期的圣炎.

Qingqing Zhu1, Qi Wang1, Xi Hu1

  • 1Department of Radiology, Sir Run Run Shaw Hospital, Zhejiang University School of Medicine, Hangzhou 310016, China.

Diagnostics (Basel, Switzerland)
|January 25, 2025
PubMed
概括

使用MRI的纹理分析 (TA) 模型显示出神经关节关节炎早期诊断的前景. 与视觉评估相比,T1WI-TA模型显著提高了诊断准确性,有助于区分早期和已确定的疾病.

关键词:
磁共振成像技术的使用这种神经性炎是神经性炎.质地分析,质地分析.

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科学领域:

  • 放射学 放射学是一门学科.
  • 医疗成像医学成像
  • 人工智能在医学中的应用

背景情况:

  • 早期诊断非放射性轴性脊柱关节炎 (nr-axSpA) 对于及时干预至关重要.
  • 磁共振成像 (MRI) 是检测神经炎的关键方式.
  • 纹理分析 (TA) 为图像评估提供了一种定量方法.

研究的目的:

  • 为了比较TA的诊断性能与早期圣炎 (nr-axSpA) 的视觉定性评估.
  • 使用TA模型评估T1加权 (T1WI) 和液体敏感,脂肪和 (Fs) T2加权 (T2WI) MRI序列的疗效.
  • 为了区分健康对照,nr-axSpA和放射性axSpA (r-axSpA) 患者.

主要方法:

  • 追溯分析92名参与者 (30对照组,32 nr-axSpA,30 r-axSpA) 进行三次性神经关节MRI.
  • 由两个独立的读者进行定性评分.
  • 开发和评估T1WI-TA和FST2WI-TA模型.
  • 与临床参考标准对比TA模型和定性得分的比较.

主要成果:

  • 无论是TA模型还是定性得分,两组之间都有显著的区别 (p < 0.05).
  • 与定性分数相比,TA模型在区分健康对照和nr-axSpA (p <0.05) 中表现优越.
  • 在区分nr-axSpA与r-axSpA (p <0.05) 方面,T1WI-TA模型的表现优于定性得分.

结论:

  • 基于MRI的T1WI-TA和FsT2WI-TA模型对于早期的关节关节炎诊断是有效的.
  • 与定性评估相比,T1WI-TA模型提高了早期诊断的有效性.
  • FsT2WI-TA模型的有效性与定性读者评分相当.