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基于核磁共振成像 (MRI) 的放射性评估结核病和布鲁塞拉脊柱炎
Wenhui Wang1,2, Zhichang Fan1,2, Junping Zhen2
1College of Medical Imaging, Shanxi Medical University, Taiyuan, China.
The Journal of international medical research
|September 1, 2023
概括
对MRI数据的放射学分析有效地区分了结核性脊髓炎 (TBS) 与布鲁塞拉脊髓炎 (BS). 机器学习模型准确地区分了培养阳性TBS和培养负的TBS,帮助诊断.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 传染性疾病 传染性疾病
背景情况:
- 结核性脊髓炎 (TBS) 和布鲁塞拉脊髓炎 (BS) 难以使用常规方法进行区分.
- 准确的区分对于适当的治疗和患者的结果至关重要.
研究的目的:
- 开发和评估机器学习模型,使用MRI和放射学特征来区分TBS和BS.
- 要区分培养阳性TBS (TBS (((+)) 和培养阴性TBS (TBS (((-)).
主要方法:
- 对56名BS患者,63名TBS患者和71名TBS患者的MRI和放射学标签的回顾性分析.
- 从T2加权脂肪抑制图像中提取的放射性特征.
- 后勤回归,随机森林和支持矢量机模型使用AUC,DCA和Hosmer-Lemeshow测试来构建和评估.
主要成果:
- 与仅使用MRI的模型相比,结合放射学或联合标签 (放射学+MRI) 的机器学习模型实现了更高的AUC (高达0.950) .
- 支持矢量机和随机森林模型在区分BS与TBS ((+) 和BS与TBS ((-)) 中表现出强的表现.
- 决策曲线分析表明,基于放射学模型的净收益优越,具有良好的预测一致性.
结论:
- 放射学分析对于将TBS与BS区分开来具有显著的潜力.
- 使用放射学特征的机器学习模型可以准确地区分文化阳性和文化阴性TBS.
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