Zhang Qian, Zhao Fanfan, Zhang Yu, Huang Man, Gong Xiangyang, Deng Xuefei
Department of Radiology, The 901st Hospital of the Joint Logistics Support Force of PLA, Hefei 230031, China.
Soochow University, Soochow 215000, China.
J Bone Oncol. 2024 Jul 29;47:100627. doi: 10.1016/j.jbo.2024.100627. eCollection 2024 Aug.
This study aims to devise and assess an automated measurement framework for lumbar pedicle screw parameters leveraging preoperative computed tomography (CT) scans and a deep learning algorithm.
A deep learning model was constructed employing a dataset comprising 1410 axial preoperative CT images of lumbar pedicles sourced from 282 patients. The model was trained to predict several screw parameters, including the axial angle and width of pedicles, the length of pedicle screw paths, and the interpedicular distance. The mean values of these parameters, as determined by two radiologists and one spinal surgeon, served as the reference standard.
The deep learning model achieved high agreement with the reference standard for the axial angle of the left pedicle (ICC = 0.92) and right pedicle (ICC = 0.93), as well as for the length of the left pedicle screw path (ICC = 0.82) and right pedicle (ICC = 0.87). Similarly, high agreement was observed for pedicle width (left ICC = 0.97, right ICC = 0.98) and interpedicular distance (ICC = 0.91). Overall, the model's performance paralleled that of manual determination of lumbar pedicle screw parameters.
The developed deep learning-based model demonstrates proficiency in accurately identifying landmarks on preoperative CT scans and autonomously generating parameters relevant to lumbar pedicle screw placement. These findings suggest its potential to offer efficient and precise measurements for clinical applications.
本研究旨在利用术前计算机断层扫描（CT）图像和深度学习算法，设计并评估一种用于腰椎椎弓根螺钉参数的自动测量框架。
使用一个包含来自282例患者的1410张腰椎椎弓根术前轴向CT图像的数据集构建深度学习模型。该模型经过训练以预测多个螺钉参数，包括椎弓根的轴向角度和宽度、椎弓根螺钉路径的长度以及椎弓根间距。由两名放射科医生和一名脊柱外科医生确定的这些参数的平均值用作参考标准。
深度学习模型在左椎弓根（组内相关系数[ICC]=0.92）和右椎弓根（ICC=0.93）的轴向角度方面，以及在左椎弓根螺钉路径长度（ICC=0.82）和右椎弓根（ICC=0.87）方面，与参考标准高度一致。同样，在椎弓根宽度（左ICC=0.97，右ICC=0.98）和椎弓根间距（ICC=0.91）方面也观察到高度一致。总体而言，该模型的性能与手动确定腰椎椎弓根螺钉参数的性能相当。
所开发的基于深度学习的模型在准确识别术前CT扫描上的标志点以及自主生成与腰椎椎弓根螺钉置入相关的参数方面表现出了能力。这些发现表明其在临床应用中提供高效且精确测量的潜力。
