Caesarendra Wahyu, Rahmaniar Wahyu, Mathew John, Thien Ady
Manufacturing Systems Engineering, Faculty of Integrated Technologies, Universiti Brunei Darussalam, Jalan Tungku Link, Gadong BE1410, Brunei.
Department of Electronic Engineering, National Taipei University of Technology, Taipei 10608, Taiwan.
Diagnostics (Basel). 2022 Feb 3;12(2):396. doi: 10.3390/diagnostics12020396.
The Cobb angle measurement of the scoliotic spine is prone to inter- and intra-observer variations in the clinical setting. This paper proposes a deep learning architecture for detecting spine vertebrae from X-ray images to evaluate the Cobb angle automatically. The public AASCE MICCAI 2019 anterior-posterior X-ray image dataset and local images were used to train and test the proposed convolutional neural network architecture. Sixty-eight landmark features of the spine were detected from the input image to obtain seventeen vertebrae on the spine. The vertebrae locations obtained were processed to automatically measure the Cobb angle. The proposed method can measure the Cobb angle with accuracies up to 93.6% and has excellent reliability compared to clinicians' measurement (intraclass correlation coefficient > 0.95). The proposed deep learning architecture may be used as a tool to augment Cobb angle measurement in X-ray images of patients with adolescent idiopathic scoliosis in a real-world clinical setting.
在临床环境中，脊柱侧弯脊柱的Cobb角测量容易出现观察者间和观察者内的差异。本文提出了一种深度学习架构，用于从X射线图像中检测脊柱椎体，以自动评估Cobb角。使用公开的AASCE MICCAI 2019前后位X射线图像数据集和本地图像来训练和测试所提出的卷积神经网络架构。从输入图像中检测出脊柱的68个地标特征，以获得脊柱上的17个椎体。对获得的椎体位置进行处理，以自动测量Cobb角。所提出的方法测量Cobb角的准确率高达93.6%，与临床医生的测量相比具有出色的可靠性（组内相关系数>0.95）。所提出的深度学习架构可作为一种工具，在实际临床环境中辅助测量青少年特发性脊柱侧弯患者X射线图像中的Cobb角。
