Department of Orthopedics, Second Affiliated Hospital (Changzheng Hospital) of Naval Medical University, Shanghai, China.
School of Electronic and Electrical Engineering, Shanghai University of Engineering Science, Shanghai, China.
Med Phys. 2023 Jul;50(7):4182-4196. doi: 10.1002/mp.16440. Epub 2023 May 10.
Cervical spinal malalignment and instability are frequently occurring pathological conditions involving neck pain, radiculopathy, and myelopathy, often requiring surgical intervention. Accurate assessment of cervical alignment and instability are essential in surgical planning and evaluating postoperative outcomes.
To automatically measure the sagittal alignment and instability of the cervical spine, we develop a novel deep-learning model by detecting landmarks on cervical radiographs.
We introduce the transformer-embedded residual network (ResNet) as the network's core to automatically identify vertebral landmarks on digital and film-transformed cervical radiographs, and simultaneously measure the segmental Cobb angle and horizontal displacement. A Transformer Module was embedded into the latent space to extract the relationship between different vertebrae. Then a Rotating Attention Module was integrated between the encoder-decoder pairs to highlight the key points and maintain more details. Finally, a Vector Loss Module was proposed to restrain the orientation of the adjacent vertebra to reduce misdetection. All images were obtained from local hospital. Digital images were split into training, validation, and test subsets (896, 225, and 353 images, respectively). Likewise, film-transformed images were split into 404, 115, and 150 images, respectively. The results of the model were compared with manual measurements.
Our deep learning algorithm achieved mean absolute difference (MAD) at a level of 2.20° and 2.33°, symmetric mean absolute error(SMAPE)at 16.63% and 19.35%, respectively, when measuring Cobb angle on digital images and films. On evaluating cervical instability, the diagnostic accuracy, sensitivity, specificity, precision, and F1-score evaluation metrics were calculated. The corresponding values were 89.80%, 86.49%, 90.68%, 71.11%, and 78.05% on digital images, and 90.00%, 83.78%, 91.15%, 75.61%, and 79.49% on film-transformed images, which were comparable to experienced surgeons. Visualization results demonstrated robust effectiveness in subjects with severe osteophytes or artifacts.
This study presents a novel and efficient deep-learning model to assist landmarks identification and angulation and displacement calculation on lateral cervical spine radiographs, and demonstrates excellent accuracy in measuring cervical alignment and sound sensitivity and specificity in cervical instability diagnosis. It should be helpful for future research and clinical applications.
颈椎失稳和失序是常见的病理情况，可引起颈部疼痛、神经根病和脊髓病，常需手术干预。准确评估颈椎的排列和不稳定情况对手术规划和评估术后结果至关重要。
通过检测颈椎 X 光片上的标志点，我们开发了一种新的深度学习模型，用于自动测量颈椎的矢状位排列和不稳定性。
我们引入了基于变压器的残差网络（ResNet）作为网络的核心，以自动识别数字和胶片转化的颈椎 X 光片上的椎体标志点，并同时测量节段 Cobb 角和水平位移。一个变压器模块被嵌入到潜在空间中，以提取不同椎体之间的关系。然后，在编码器-解码器对之间集成了一个旋转注意力模块，以突出关键点并保持更多细节。最后，提出了一个向量损失模块来限制相邻椎体的方向，以减少误检。所有图像均来自当地医院。数字图像被分为训练集、验证集和测试集（分别为 896、225 和 353 张图像）。同样，胶片转化的图像被分为 404、115 和 150 张图像。模型的结果与手动测量进行了比较。
我们的深度学习算法在测量数字图像和胶片上的 Cobb 角时，平均绝对差值（MAD）分别达到 2.20°和 2.33°，对称平均绝对误差（SMAPE）分别达到 16.63%和 19.35%。在评估颈椎不稳定时，计算了诊断准确性、敏感度、特异度、精度和 F1 评分评估指标。在数字图像上，相应的值分别为 89.80%、86.49%、90.68%、71.11%和 78.05%，在胶片转化的图像上，相应的值分别为 90.00%、83.78%、91.15%、75.61%和 79.49%，与经验丰富的外科医生相当。可视化结果表明，该模型在存在严重骨赘或伪影的患者中具有强大的有效性。
本研究提出了一种新颖而高效的深度学习模型，用于辅助颈椎侧位 X 光片上的标志点识别和角度及位移计算，并在测量颈椎排列方面表现出优异的准确性，在诊断颈椎不稳定方面具有良好的灵敏度和特异性。它应该有助于未来的研究和临床应用。
