使用卷积神经网络自动测量青少年特异性脊椎病的科布角,使用卷积神经网络
Yoshihiro Maeda1, Takeo Nagura1, Masaya Nakamura1
1Department of Orthopedic Surgery, Keio University School of Medicine, Tokyo, Japan.
Scientific reports
|September 4, 2023
概括
这项研究引入了一种人工智能方法,用于使用X射线图像测量脊椎病变的严重程度. 人工智能工具准确地测量了科布角 (CA),与医生手动测量相比,显示出高可靠性.
科学领域:
- 放射学 放射学是一门学科.
- 医疗成像医学成像
- 医疗保健中的人工智能
背景情况:
- 脊柱病的诊断和治疗依赖于准确的科布角 (CA) 测量.
- 手动的CA测量可能是主观的,耗时的.
- 青少年异常学脊椎病 (AIS) 需要精确的监测.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 用于自动检测脊椎和在脊柱X射线上测量CA.
- 评估人工智能算法的可靠性和准确性,与经验丰富的临床医生手动测量相比.
主要方法:
- 利用了来自AIS患者的1021个全长脊柱X射线数据集.
- 一个CNN模型接受了自动脊椎识别和CA计算的训练.
- 人工智能测量结果与六名骨科专家的手动测量结果进行了比较,使用了类内相关系数 (ICC).
主要成果:
- 人工智能算法与手工测量显示出良好的或良好的一致性 (主要曲线的ICC = 0.973).
- 人工智能和临床医生之间的平均误差在不同的角度大小中是一致的.
- 人工智能测量显示高相关性,无论CA大小,临床医生的经验,或患者的姿势.
结论:
- 拟议的AI方法为AIS患者的科布角测量提供了一种可靠和自动化的方法.
- 这项技术有可能简化脊柱形脊柱病的评估,提高诊断的一致性.
- 人工智能工具在脊柱形评估中显示出临床应用的前景.
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