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在临床实践中使用卷积神经网络进行脊椎病的放射分析

Ha Yun Oh, Tae Kun Kim, Yun Sun Choi

Journal of the Korean Society of Radiology

|October 17, 2024

概括

使用卷积神经网络 (CNN) 的自动科布角测量 (ACAM) 提供可靠和准确的脊椎病评估,特别是在轻度至中度病例中. 与手工方法相比,这种人工智能工具显著减少了测量时间.

科学领域:

医疗成像医学成像
医疗保健中的人工智能
整形外科整形外科整形外科

背景情况:

脊柱脊椎病的评估依赖于从脊柱放射图中准确的科布角 (CA) 测量.
手动的CA测量可能耗时,并且会受到观察者之间的变化.
正在开发自动化方法以提高效率和一致性.

研究的目的:

评估使用卷积神经网络 (CNN) 的自动化科布角测量 (ACAM) 系统的可靠性和准确性.
为了比较ACAM与人类观察者的测量时间,以评估脊椎病.
为了确定ACAM在不同脊柱硬化严重程度和脊柱状况的表现.

主要方法:

ACAM被应用于411个脊柱X射线图的患者怀疑脊椎变.
科布角测量由ACAM和两个人类观察员 (放射科医生) 进行.
观察者之间的可靠性,相关性,准确性和测量时间使用ICC和斯皮尔曼等级相关性进行了统计分析.

主要成果:

通过专家测量,ACAM显示出出色的可靠性 (ICC=0.976) 和高相关性 (Spearman's=0.948),平均差异为1.1度.
观察到高整体精度 (88.2%),在轻度 (92.2%) 和中度 (96%) 脊椎病中表现优异.
与人类观察者相比,ACAM显著减少了大约50%的测量时间 (p < 0.001).

结论:

使用CNN的自动科布角测量提供了一种可靠和准确的工具,用于评估轻度至中度脊椎病.
虽然对于脊柱不对称性和严重的脊柱脊椎病的准确性较低,但ACAM大大减少了评估时间.
在提高脊柱体脊柱病诊断和监测的效率方面,ACAM显示出有前途.

关键词:

科布的角度科布的角度卷积神经网络是一个卷积神经网络.放射学放射学放射学放射学脊椎形症是什么? 脊椎形症是什么?

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