Yu Qi-Shuai, Shan Jing-Yang, Ma Jie, Gao Gan, Tao Ben-Zhang, Qiao Guang-Yu, Zhang Jian-Ning, Wang Ting, Zhao Yong-Fei, Qin Xiao-Lin, Yin Yi-Heng
Department of Neurosurgery, the First Medical Center, Chinese PLA General Hospital, Beijing, China.
School of Medicine, Nankai University, Tianjin, China.
Sci Data. 2025 Jul 1;12(1):1080. doi: 10.1038/s41597-025-05403-z.
Multi-modal and multi-view imaging is essential for diagnosis and assessment of cervical spondylosis. Deep learning has increasingly been developed to assist in diagnosis and assessment, which can help improve clinical management and provide new ideas for clinical research. To support the development and testing of deep learning models for cervical spondylosis, we have publicly shared a multi-modal and multi-view imaging dataset of cervical spondylosis, named MMCSD. This dataset comprises MRI and CT images from 250 patients. It includes axial bone and soft tissue window CT scans, sagittal T1-weighted and T2-weighted MRI, as well as axial T2-weighted MRI. Neck pain is one of the most common symptoms of cervical spondylosis. We use the MMCSD to develop a deep learning model for predicting postoperative neck pain in patients with cervical spondylosis, thereby validating its usability. We hope that the MMCSD will contribute to the advancement of neural network models for cervical spondylosis and neck pain, further optimizing clinical diagnostic assessments and treatment decision-making for these conditions.
多模态和多视角成像对于颈椎病的诊断和评估至关重要。深度学习已越来越多地被开发用于辅助诊断和评估，这有助于改善临床管理并为临床研究提供新思路。为支持颈椎病深度学习模型的开发和测试，我们已公开发布了一个名为MMCSD的颈椎病多模态和多视角成像数据集。该数据集包含250名患者的MRI和CT图像。它包括轴向骨和软组织窗CT扫描、矢状位T1加权和T2加权MRI以及轴向T2加权MRI。颈部疼痛是颈椎病最常见的症状之一。我们使用MMCSD开发了一个深度学习模型，用于预测颈椎病患者术后的颈部疼痛，从而验证其可用性。我们希望MMCSD将有助于推进针对颈椎病和颈部疼痛的神经网络模型，进一步优化这些病症的临床诊断评估和治疗决策。
