Cervical spinal cord compression, defined as spinal cord deformity and severe narrowing of the spinal canal in the cervical region, can lead to severe clinical consequences, including intractable pain, sensory disturbance, paralysis, and even death, and may require emergent intervention to prevent negative outcomes. Despite the critical nature of cord compression, no automated tool is available to alert clinical radiologists to the presence of such findings. This study aims to demonstrate the ability of a vision transformer (ViT) model for the accurate detection of cervical cord compression.

A clinically diverse cohort of 142 cervical spine MRIs was identified, 34% of which were normal or had mild stenosis, 31% with moderate stenosis, and 35% with cord compression. Utilizing gradient-echo images, slices were labeled as no cord compression/mild stenosis, moderate stenosis, or severe stenosis/cord compression. Segmentation of the spinal canal was performed and confirmed by neuroradiology faculty. A pretrained ViT model was fine-tuned to predict section-level severity by using a train:validation:test split of 60:20:20. Each examination was assigned an overall severity based on the highest level of section severity, with an examination labeled as positive for cord compression if ≥1 section was predicted in the severe category. Additionally, 2 convolutional neural network (CNN) models (ResNet50, DenseNet121) were tested in the same manner.

The ViT model outperformed both CNN models at the section level, achieving section-level accuracy of 82%, compared with 72% and 78% for ResNet and DenseNet121, respectively. ViT patient-level classification achieved accuracy of 93%, sensitivity of 0.90, positive predictive value of 0.90, specificity of 0.95, and negative predictive value of 0.95. Receiver operating characteristic area under the curve was greater for ViT than either CNN.

This classification approach using a ViT model and rules-based classification accurately detects the presence of cervical spinal cord compression at the patient level. In this study, the ViT model outperformed both conventional CNN approaches at the section and patient levels. If implemented into the clinical setting, such a tool may streamline neuroradiology workflow, improving efficiency and consistency.

颈髓受压，定义为脊髓畸形和颈部椎管严重狭窄，可导致严重的临床后果，包括顽固性疼痛、感觉障碍、瘫痪，甚至死亡，可能需要紧急干预以防止出现负面影响。结果。尽管脊髓压迫具有至关重要的性质，但没有可用的自动化工具来提醒临床放射科医生此类发现的存在。本研究旨在展示视觉变换器 (ViT) 模型准确检测颈髓压迫的能力。

确定了 142 个颈椎 MRI 的临床多样化队列，其中 34% 正常或有轻度狭窄，31% 有中度狭窄，35% 有脊髓压迫。利用梯度回波图像，切片被标记为无脊髓压迫/轻度狭窄、中度狭窄或严重狭窄/脊髓压迫。椎管的分割由神经放射学教师进行并确认。通过使用 60:20:20 的训练：验证：测试分割，对预训练的 ViT 模型进行微调以预测部分级别的严重性。每次检查都根据部位严重程度的最高级别分配总体严重程度，如果在严重类别中预测≥1个部位，则检查标记为脊髓受压阳性。此外，还以相同的方式测试了 2 个卷积神经网络 (CNN) 模型（ResNet50、DenseNet121）。

ViT 模型在部分级别上优于两种 CNN 模型，实现了 82% 的部分级别准确率，而 ResNet 和 DenseNet121 的准确率分别为 72% 和 78%。 ViT 患者级别分类的准确度为 93%，敏感性为 0.90，阳性预测值为 0.90，特异性为 0.95，阴性预测值为 0.95。 ViT 的接收者操作特征曲线下面积大于任一 CNN。

这种分类方法使用 ViT 模型和基于规则的分类，可以准确地检测患者层面是否存在颈髓压迫。在这项研究中，ViT 模型在切片和患者层面均优于传统的 CNN 方法。如果应用于临床环境，这样的工具可以简化神经放射学工作流程，提高效率和一致性。