组内相关
柯布角
脊柱侧凸
卷积神经网络
科布
特发性脊柱侧凸
相关系数
人工智能
可靠性(半导体)
计算机科学
分割
人工神经网络
口腔正畸科
医学
皮尔逊积矩相关系数
模式识别(心理学)
再现性
数学
统计
外科
机器学习
物理
生物
功率(物理)
量子力学
遗传学
作者
Yoshihiro Maeda,Takeo Nagura,Masaya Nakamura,Kota Watanabe
标识
DOI:10.1038/s41598-023-41821-y
摘要
This study proposes a convolutional neural network method for automatic vertebrae detection and Cobb angle (CA) measurement on X-ray images for scoliosis. 1021 full-length X-ray images of the whole spine of patients with adolescent idiopathic scoliosis (AIS) were used for training and segmentation. The proposed AI algorithm's results were compared with those of the manual method by six doctors using the intraclass correlation coefficient (ICC). The ICCs recorded by six doctors and AI were excellent or good, with a value of 0.973 for the major curve in the standing position. The mean error between AI and doctors was not affected by the angle size, with AI tending to measure 1.7°-2.2° smaller than that measured by the doctors. The proposed method showed a high correlation with the doctors' measurements, regardless of the CA size, doctors' experience, and patient posture. The proposed method showed excellent reliability, indicating that it is a promising automated method for measuring CA in patients with AIS.
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