1Department of Orthopedic Surgery, Chung-Ang University Hospital, College of Medicine, Chung-Ang University, Dongjak-gu, Seoul.
2Department of Orthopedic Surgery, Seoul National University College of Medicine and Seoul National University Bundang Hospital, Gyeonggi-do, Seoul, South Korea.
J Neurosurg Spine. 2023 Jun 9;39(3):329-334. doi: 10.3171/2023.5.SPINE23293. Print 2023 Sep 1.
Interspinous motion (ISM) is a representative method for evaluating the functional fusion status following anterior cervical discectomy and fusion (ACDF) surgery, but the associated measuring difficulty and potential errors in the clinical setting remain concerns. The aim of this study was to investigate the feasibility of a deep learning-based segmentation model for measuring ISM in patients who underwent ACDF surgery.
This study is a retrospective analysis of flexion-extension dynamic cervical radiographs from a single institution and a validation of a convolutional neural network (CNN)-based artificial intelligence (AI) algorithm for measuring ISM. Data from 150 lateral cervical radiographs from the normal adult population were used to train the AI algorithm. A total of 106 pairs of dynamic flexion-extension radiographs from patients who underwent ACDF at a single institution were analyzed and validated for measuring ISM. To evaluate the agreement power between human experts and the AI algorithm, the authors assessed the interrater reliability using the intraclass correlation coefficient and root mean square error (RMSE) and performed a Bland-Altman plot analysis. They processed 106 pairs of radiographs from ACDF patients into the AI algorithm for autosegmenting the spinous process created using 150 normal population radiographs. The algorithm automatically segmented the spinous process and converted it to a binary large object (BLOB) image. The rightmost coordinate value of each spinous process from the BLOB image was extracted, and the pixel distance between the upper and lower spinous process coordinate value was calculated. The AI-measured ISM was calculated by multiplying the pixel distance by the pixel spacing value included in the DICOM tag of each radiograph.
The AI algorithm showed a favorable prediction power for detecting spinous processes with an accuracy of 99.2% in the test set radiographs. The interrater reliability between the human and AI algorithm of ISM was 0.88 (95% CI 0.83-0.91), and its RMSE was 0.68. In the Bland-Altman plot analysis, the 95% limit of interrater differences ranged from 0.11 to 1.36 mm, and a few observations were outside the 95% limit. The mean difference between observers was 0.02 ± 0.68 mm.
This novel CNN-based autosegmentation algorithm for measuring ISM in dynamic cervical radiographs showed strong agreement power to expert human raters and could help clinicians to evaluate segmental motion following ACDF surgery in clinical settings.
棘突间活动度（ISM）是评估前路颈椎间盘切除融合术（ACDF）后功能融合状态的一种代表性方法，但在临床环境中，其相关测量难度和潜在误差仍然存在。本研究旨在探讨基于深度学习的分割模型在测量 ACDF 术后患者 ISM 中的可行性。
本研究为回顾性分析单中心屈伸位动态颈椎 X 线片，并验证基于卷积神经网络（CNN）的人工智能（AI）算法在测量 ISM 中的应用。数据来自 150 例正常成人人群的侧位颈椎 X 线片，用于训练 AI 算法。分析并验证了来自单中心行 ACDF 术的 106 对动态屈伸位 X 线片，以测量 ISM。为评估人类专家与 AI 算法之间的一致性，作者采用组内相关系数和均方根误差（RMSE）评估了观察者间的可靠性，并进行了 Bland-Altman 图分析。作者将 106 对 ACDF 患者的 X 线片输入 AI 算法，以自动对使用 150 例正常人群 X 线片创建的棘突进行分割。该算法自动分割棘突并将其转换为二进制大对象（BLOB）图像。从 BLOB 图像中提取每个棘突的最右侧坐标值，并计算上下棘突坐标值之间的像素距离。AI 测量的 ISM 乘以每张 X 线片 DICOM 标签中包含的像素间距值进行计算。
AI 算法在测试集 X 线片中对棘突的检测具有良好的预测能力，准确率为 99.2%。ISM 人类观察者与 AI 算法的观察者间可靠性为 0.88（95%CI 0.83-0.91），其 RMSE 为 0.68。在 Bland-Altman 图分析中，观察者间差异的 95%限值范围为 0.11 至 1.36mm，有少数观察结果超出 95%限值。观察者间的平均差异为 0.02±0.68mm。
本研究提出了一种用于测量动态颈椎 X 线片中 ISM 的新型基于 CNN 的自动分割算法，与专家人类观察者具有较强的一致性，可帮助临床医生在临床环境中评估 ACDF 术后的节段运动。
