基于深度学习的人工智能算法在寰椎椎弓根螺钉自动规划中的可行性研究

Feasibility study of artificial intelligence algorithm based on deep learning in C1 pedicle screw automatic planning

目的探讨人工智能(artificial intelligence,AI)辅助寰椎平行于矢状面的椎弓根螺钉自动规划中的安全性和准确性。方法选取2020年1月—2023年12月在自贡市第四人民医院完成颈椎CT扫描人群。采用随机数字表法将纳入人群的80%作为训练模型(训练组),20%作为验证模型(验证组)。将训练组的颈椎CT原始数据导入ITK-SNAP软件进行特征点标记,并选取4个特征点。采用空间关键点定位算法对训练组的寰椎标记点进行训练,以获取4个特征点的加权函数模型。最终将算法进行编译,形成可视化界面,导入验证组的寰椎CT数据计算椎弓根螺钉路径。结果共纳入CT扫描人群500例。其中,训练组400例,验证组100例。空间关键点的平均定位误差为(0.47±0.16)mm;规划的椎弓根螺钉中心线与椎弓根内边缘平均距离为(2.86±0.12)mm。对于寰椎椎弓根足够容纳直径3.5 mm的椎体,能安全进行平行于矢状面的椎弓根螺钉置入和三维显示,无皮质突破的情况。结论对于寰椎平行于矢状面的椎弓根螺钉规划,基于前、后结节和双侧切点的空间定位算法进行训练,能获得安全和准确的椎弓根螺钉AI规划路径;为骨科机器人自动置钉提供理论基础。对于寰椎椎弓根狭窄或异型的椎体,需要进一步扩大训练数据,以扩大该算法的适应范围和提高其准确度。

Objective To investigating the safety and accuracy of artificial intelligence(AI)assisted automatic planning of pedicle screws parallel to sagittal plane for C1.Methods The subjects who completed cervical CT scan in Zigong Fourth People’s Hospital btween January 2020 and December 2023 were selected.The subjects who completed cervical CT scan were randomly divided into two groups using a random number table method.Among them,80%were used as the training model(training group),and 20%were used as the validation model(validation group).The original cervical CT data of the training group were imported into ITK-SNAP software to mark the feature points.Four feature points were selected.In order to obtain the weighted function model of the four feature points,training group were trained with the spatial key point location algorithm.pedicle trajectory based on the four key points obtained.Finally,the algorithm was compiled to form a visual interface,and imported into the verification group of annular vertebral CT data to calculate the pedicle screw trajectory.Results A total of 500 patients were included.Among them,there were 400 cases in the training group and 100 cases in the validation group.The average positioning error of spatial key points is(0.47±0.16)mm.The average distance between the planned pedicle screw center line and the internal edge of the pedicle was(2.86±0.12)mm.Pedicle screw placement parallel to the sagittal plane and 3D display can be safely performed for the C1 pedicle that is large enough to accommodate a 3.5 mm diameter screw without cortical breakthrough.Conclusions For pedicle screw planning parallel to the sagittal plane in C1,training based on the spatial positioning algorithm of anterior and posterior tubercles and bilateral tangential points can obtain a safe and accurate pedicle screw trajectory.It provides theoretical basis for orthopedic robot automatic screw placement.For vertebral bodies with narrow or deformed pedicles,further expansion of the training data is needed to expand the adaptive range and improve the accuracy of the algorithm.