LAUSR

BACKGROUND 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. PURPOSE 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. METHODS 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. RESULTS 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. CONCLUSION 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.