矢状位椎间旋转运动：基于深度学习的颈椎侧位屈伸中立位片测量。
Sagittal intervertebral rotational motion: a deep learning-based measurement on flexion-neutral-extension cervical lateral radiographs.
机构信息
The Second School of Clinical Medicine, Zhejiang Chinese Medical University, Hangzhou, 310053, Zhejiang, China.
Rehabilitation Medicine Center, Department of Radiology, Zhejiang Provincial People's Hospital, Affiliated People's Hospital, Hangzhou Medical College, Hangzhou, 310014, Zhejiang, China.
出版信息
BMC Musculoskelet Disord. 2022 Nov 8;23(1):967. doi: 10.1186/s12891-022-05927-0.
BACKGROUND
The analysis of sagittal intervertebral rotational motion (SIRM) can provide important information for the evaluation of cervical diseases. Deep learning has been widely used in spinal parameter measurements, however, there are few investigations on spinal motion analysis. The purpose of this study is to develop a deep learning-based model for fully automated measurement of SIRM based on flexion-neutral-extension cervical lateral radiographs and to evaluate its applicability for the flexion-extension (F/E), flexion-neutral (F/N), and neutral-extension (N/E) motion analysis.
METHODS
A total of 2796 flexion, neutral, and extension cervical lateral radiographs from 932 patients were analyzed. Radiographs from 100 patients were randomly selected as the test set, and those from the remaining 832 patients were used for training and validation. Landmarks were annotated for measuring SIRM at five segments from C2/3 to C6/7 on F/E, F/N, and N/E motion. High-Resolution Net (HRNet) was used as the main structure to train the landmark detection network. Landmark performance was assessed according to the percentage of correct key points (PCK) and mean of the percentage of correct key points (MPCK). Measurement performance was evaluated by intra-class correlation coefficient (ICC), Pearson correlation coefficient, mean absolute error (MAE), root mean square error (RMSE), and Bland-Altman plots.
RESULTS
At a 2-mm distance threshold, the PCK for the model ranged from 94 to 100%. Compared with the reference standards, the model showed high accuracy for SIRM measurements for all segments on F/E and F/N motion. On N/E motion, the model provided reliable measurements from C3/4 to C6/7, but not C2/3. Compared with the radiologists' measurements, the model showed similar performance to the radiologists.
CONCLUSIONS
The developed model can automatically measure SIRM on flexion-neutral-extension cervical lateral radiographs and showed comparable performance with radiologists. It may provide rapid, accurate, and comprehensive information for cervical motion analysis.
背景
矢状位椎间旋转运动（SIRM）的分析可为颈椎疾病的评估提供重要信息。深度学习已广泛应用于脊柱参数测量，但对脊柱运动分析的研究较少。本研究旨在开发一种基于颈椎侧位屈伸位片的 SIRM 全自动测量的深度学习模型，并评估其在屈伸位（F/E）、前屈中立后伸位（F/N）和中立后伸位（N/E）运动分析中的适用性。
方法
分析了 932 例患者的 2796 例颈椎侧位屈伸位片。100 例患者的 X 线片被随机选择为测试集，其余 832 例患者的 X 线片用于训练和验证。在 F/E、F/N 和 N/E 运动的五个节段（C2/3 至 C6/7）上测量 SIRM 时，对标志点进行标注。使用高分辨率网络（HRNet）作为主要结构来训练标志点检测网络。根据关键点正确百分比（PCK）和平均关键点正确百分比（MPCK）评估标志点性能。采用组内相关系数（ICC）、Pearson 相关系数、平均绝对误差（MAE）、均方根误差（RMSE）和 Bland-Altman 图评估测量性能。
结果
在 2mm 距离阈值下，模型的 PCK 范围为 94%至 100%。与参考标准相比，该模型在 F/E 和 F/N 运动的所有节段上均能准确测量 SIRM。在 N/E 运动中，该模型可以为 C3/4 至 C6/7 提供可靠的测量结果，但 C2/3 除外。与放射科医生的测量结果相比，该模型与放射科医生的测量结果具有相似的性能。
结论
所开发的模型可自动测量颈椎侧位屈伸位片上的 SIRM，其性能与放射科医生相当。它可以为颈椎运动分析提供快速、准确和全面的信息。
Zotero 插件