Boonrod Arunnit, Boonrod Artit, Meethawolgul Atthaphon, Twinprai Prin
Department of Radiology, Khon Kaen University, Khon Kaen, 40002, Thailand.
AI and Informatics in Medical Imaging (AIIMI) Research Group, Faculty of Medicine, Khon Kaen University, Khon Kaen, 40002, Thailand.
Heliyon. 2022 Aug 24;8(8):e10372. doi: 10.1016/j.heliyon.2022.e10372. eCollection 2022 Aug.
Traumatic spinal cord injury (TSI) is a leading cause of morbidity and mortality worldwide, with the cervical spine being the most affected. Delayed diagnosis carries a risk of morbidity and mortality. However, cervical spine CT scans are time-consuming, costly, and not always available in general care. In this study, deep learning was used to assess and improve the detection of cervical spine injuries on lateral radiographs, the most widely used screening method to help physicians triage patients quickly and avoid unnecessary CT scans.
Lateral neck or lateral cervical spine radiographs were obtained for patients who underwent CT scan of cervical spine. Ground truth was determined based on CT reports. CiRA CORE, a codeless deep learning program, was used as a training and testing platform. YOLO network models, including V2, V3, and V4, were trained to detect cervical spine injury. The diagnostic accuracy, sensitivity, and specificity of the model were calculated.
A total of 229 radiographs (129 negative and 100 positive) were selected for inclusion in our study from a list of 625 patients with cervical spine CT scans, 181 (28.9%) of whom had cervical spine injury. The YOLO V4 model performed better than the V2 or V3 (AUC = 0.743), with sensitivity, specificity, and accuracy of 80%, 72% and 75% respectively.
Deep learning can improve the accuracy of lateral c-spine or neck radiographs. We anticipate that this will assist clinicians in quickly triaging patients and help to minimize the number of unnecessary CT scans.
创伤性脊髓损伤（TSI）是全球发病和死亡的主要原因，颈椎受影响最为严重。延迟诊断存在发病和死亡风险。然而，颈椎CT扫描耗时、成本高，且在普通医疗中并非总是可用。在本研究中，深度学习被用于评估和改进颈椎侧位X线片上颈椎损伤的检测，颈椎侧位X线片是最广泛使用的筛查方法，可帮助医生快速对患者进行分诊并避免不必要的CT扫描。
对接受颈椎CT扫描的患者获取颈部侧位或颈椎侧位X线片。根据CT报告确定真实情况。CiRA CORE，一个无代码深度学习程序，被用作训练和测试平台。训练包括V2、V3和V4的YOLO网络模型以检测颈椎损伤。计算模型的诊断准确性、敏感性和特异性。
从625例颈椎CT扫描患者列表中，共选择229张X线片（129张阴性和100张阳性）纳入我们的研究，其中181例（28.9%）有颈椎损伤。YOLO V4模型的表现优于V2或V3（AUC = 0.743），敏感性、特异性和准确性分别为80%、72%和75%。
深度学习可以提高颈椎侧位或颈部X线片的准确性。我们预计这将有助于临床医生快速对患者进行分诊，并有助于减少不必要的CT扫描数量。
