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机器学习标准化积分区域算法的应用,用于测量脊椎病变
Shuman Han1, Hongyu Zhao1, Yi Zhang2
1Department of Radiology, The Third Affiliated Hospital of Hebei Medical University, Shijiazhuang 139 Ziqiang Road, Shijiazhuang, 050051, Hebei, China.
Scientific reports
|November 7, 2023
概括
这项研究引入了一种使用机器学习的计算机视觉方法,可以从X射线图像中自动测量脊椎病的严重程度. 新的标准化积分面积算法与科布角呈现出良好的一致性,有助于高效地查脊椎病.
科学领域:
- 医疗成像医学成像
- 计算机视觉 计算机视觉
- 机器学习 机器学习
背景情况:
- 脊柱变形的脊柱病需要准确的测量来规划治疗.
- 传统的方法来测量脊椎病的严重程度,比如科布角度,可能是耗时和主观的.
研究的目的:
- 开发和验证一种基于计算机视觉的方法,用于自动测量脊柱脊椎病的程度.
- 通过使用X射线图像来评估机器学习算法的有效性,以量化脊柱形严重程度.
主要方法:
- 计算机视觉算法利用直方图平衡和翻转方法被用来识别脊柱曲线.
- 脊椎的程度是用标准化的积分面积来量化,该积分面积是从合适的立方线上计算出来的.
- 该算法在204名异常学脊椎病患者的X射线上进行了测试,结果与手动科布角测量相比较.
主要成果:
- 标准化积分面积算法证明了与手动科布角测量的强正相关性 (斯皮尔曼r=0.643,P<0.001).
- 该算法在评估轻度,中度和严重病例的脊椎病程度方面表现出良好的一致性.
- 接收器运行特征 (ROC) 曲线分析表明,对分类中度 (AUC=0.865) 和严重 (AUC=0.873) 脊椎病的准确性很高.
结论:
- 开发的基于机器学习的计算机视觉方法提供了一种快速有效的方法来评估脊椎病的严重程度.
- 标准化积分面积算法是大数据集中查脊椎病的宝贵工具,并补充了传统的科布角测量.
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