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使用机器学习的自主腰椎椎弓根螺钉规划：一项验证研究。

Autonomous lumbar spine pedicle screw planning using machine learning: A validation study.

作者信息

Siemionow Kris B, Forsthoefel Craig W, Foy Michael P, Gawel Dominik, Luciano Christian J

机构信息

Department of Research, Holo Surgical Inc, Chicago, IL, USA.

Department of Orthopaedics, University of Illinois, Chicago, IL, USA.

出版信息

J Craniovertebr Junction Spine. 2021 Jul-Sep;12(3):223-227. doi: 10.4103/jcvjs.jcvjs_94_21. Epub 2021 Sep 8.

DOI:10.4103/jcvjs.jcvjs_94_21
PMID:34728987
原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC8501821/
Abstract

INTRODUCTION

Several techniques for pedicle screw placement have been described including freehand techniques, fluoroscopy assisted, computed tomography (CT) guidance, and robotics. Image-guided surgery offers the potential to combine the benefits of CT guidance without the added radiation. This study investigated the ability of a neural network to place lumbar pedicle screws with the correct length, diameter, and angulation autonomously within radiographs without the need for human involvement.

MATERIALS AND METHODS

The neural network was trained using a machine learning process. The method combines the previously reported autonomous spine segmentation solution with a landmark localization solution. The pedicle screw placement was evaluated using the Zdichavsky, Ravi, and Gertzbein grading systems.

RESULTS

In total, the program placed 208 pedicle screws between the L1 and S1 spinal levels. Of the 208 placed pedicle screws, 208 (100%) had a Zdichavsky Score 1A, 206 (99.0%) of all screws were Ravi Grade 1, and Gertzbein Grade A indicating no breech. The final two screws (1.0%) had a Ravi score of 2 (<2 mm breech) and a Gertzbein grade of B (<2 mm breech).

CONCLUSION

The results of this experiment can be combined with an image-guided platform to provide an efficient and highly effective method of placing pedicle screws during spinal stabilization surgery.

摘要

引言

已经描述了几种椎弓根螺钉置入技术，包括徒手技术、透视辅助、计算机断层扫描（CT）引导和机器人技术。图像引导手术有可能结合CT引导的优点而不增加辐射。本研究调查了神经网络在无需人工干预的情况下，在X线片内自主置入长度、直径和角度正确的腰椎椎弓根螺钉的能力。

材料与方法

使用机器学习过程对神经网络进行训练。该方法将先前报道的自主脊柱分割解决方案与地标定位解决方案相结合。使用兹迪查夫斯基、拉维和格茨贝恩分级系统评估椎弓根螺钉置入情况。

结果

该程序总共在L1至S1椎体节段置入了208枚椎弓根螺钉。在置入的208枚椎弓根螺钉中，208枚（100%）的兹迪查夫斯基评分为1A，所有螺钉中有206枚（99.0%）为拉维1级，格茨贝恩A级表示无突破。最后两枚螺钉（1.0%）的拉维评分为2（突破<2mm），格茨贝恩评分为B（突破<2mm）。

结论

本实验结果可与图像引导平台相结合，为脊柱稳定手术中置入椎弓根螺钉提供一种高效且有效的方法。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/1255733261c7/JCVJS-12-223-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/205af0b5933b/JCVJS-12-223-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/b9bc08a20092/JCVJS-12-223-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/1255733261c7/JCVJS-12-223-g003.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/205af0b5933b/JCVJS-12-223-g001.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/b9bc08a20092/JCVJS-12-223-g002.jpg
https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1c0b/8501821/1255733261c7/JCVJS-12-223-g003.jpg

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