Chinese Journal of Tissue Engineering Research ›› 2024, Vol. 28 ›› Issue (30): 4915-4920.doi: 10.12307/2024.640
Tao Guangyi1, Wang Linzi1, Yang Bin2, Huang Junqing2
Received:
2023-08-21
Accepted:
2023-09-20
Online:
2024-10-28
Published:
2023-12-28
Contact:
Huang Junqing, Master’s supervisor, Chief physician, Department of Pain, Henan Provincial Hospital of TCM/Second Affiliated Hospital of Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China
About author:
Tao Guangyi, Master candidate, College of Bone Injury, Henan University of Chinese Medicine, Zhengzhou 450000, Henan Province, China
Supported by:
CLC Number:
Tao Guangyi, Wang Linzi, Yang Bin, Huang Junqing. Research hotspots of artificial intelligence in the field of spinal deformity: visual analysis[J]. Chinese Journal of Tissue Engineering Research, 2024, 28(30): 4915-4920.
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2.4.2 文献共被引分析结果 对文献共被引展开深入分析,利用VOSviewer 1.6.19软件,对人工智能在脊柱畸形领域从2004-2023年共被引次数前10名的文献进行分析[8-17],表4总结了人工智能在脊柱畸形领域前10位的共被引文献。这些文献都是这一领域的核心文献,它们的学术影响很大,某种程度上反映了这一领域的研究热点和前沿,是进行这一领域研究的重要参考。在被引数前10名的论文中,有2篇在《European Spine Journal》(期刊影响因子/JCR分区为2.8/Q1)杂志发表,而剩下的文章均发表在不同期刊。这些文章均发表于2015-2019年,排名前10位的共被引文献的发表年份,代表此领域处于快速发展阶段,研究热点新颖。"
共被引频次最高的文献为HORNG等[8](2019,Comput Math Methods Med),共被引频次为21次,发表时间为2019年,该文章提出了一个自动系统测量脊柱弯曲使用前后(AP)视图脊柱X射线图像,采用多种U型架构(深度学习卷积神经网络的一种成熟模式)对椎骨进行分割;最后将椎骨的分割结果重构为完整的分割后的脊柱图像,并根据Cobb角(评估脊柱侧弯的角度)准则计算脊柱曲率。此文章共被引频次最高且发表年份新,可以看出该卷积神经网络的自动化方法受到此领域诸多学者的认可。 共被引频次第5名的文献提出了一种基于变形U型架构脊柱旁肌肉自动测量系统,它的残差模块可以在保留图像细节的同时直接返回梯度,使模型更容易训练,使磁共振图像中棘旁肌能够更加准确地分割[12]。 共被引频次第2,4,6,7名的文献也是提出一种训练策略和框架,利用U型架构(U-net,深度学习卷积神经网络的一种成熟模式)来自动测量影像学参数(Cobb角、棘旁肌准确分割等等),毫无疑问这是人工智能在脊柱畸形领域最大的研究热点。 共被引频次第3名的文献提出了多视图相关网络(Multi-View Correlation Network,MVC-Net)架构,该架构可以为多视图X射线片中的脊柱曲率估计提供全自动的端到端框架,使得网络能够通过利用两个视图的结构依赖性来缓解遮挡问题,为临床提供了有效的脊柱曲度评估框架[9, 11,13-14]。 共被引频次第8名的文献通过术前计算机断层扫描中的单个椎骨与术后电子计算机X射线片断层扫描技术融合来评估使用机器人引导的外科医生计划的可重复性,得出结论机器人引导可以高度准确地执行术前计划,从而实现准确的螺钉放置[15]。 共被引频次第9名的文献也关注于多视图相关网络(Multi-View Correlation Network,MVC-Net)架构,证明了MVC-Net在多视角X射线片中能提供准确的Cobb角度估计[16]。 共被引频次第10名的文献也开发了深度学习算法(但并非卷积神经网络),用于使用赤裸背部图像自动筛查脊柱侧凸和Cobb角等[17]。 文章采用VOSviewer 1.6.19软件生成参考文献共被引图谱,见图5。共被引文献被分为3种颜色(3大聚类),即人工智能在脊柱畸形领域的3大热点,包括利用U型架构(U-net,深度学习卷积神经网络的一种成熟模式)来自动测量影像学参数(Cobb角、棘旁肌准确分割等)、多视图相关网络(MVC-Net)架构(即脊柱曲度评估框架)、机器人引导脊柱手术。"
2.4.3 高被引文献深度分析结果 结合图6可知,文献共被引爆发研究共15篇文献,根据爆发时间分为2大的阶段:①第一阶段是2012-2016年,代表文献是KANTELHARDT等[18]于2011年发表,主要研究机器人引导和经皮椎弓根螺钉置入手术;②第二阶段是2019-2023年,代表文献是WU等[10]于2018年发表,主要研究多视图相关网络(Multi-View Extrapolation Net)架构在多视图X射线片中能够稳健准确地估计Cobb角度。对高共被引文献的年份进行分析,发现高共被引文献发表的时间位于2012-2021年。每个时间段都有相对应的研究热点,研究热点也随着时间发生改变。如在2012-2016时间段,机器人引导脊柱手术是其主要研究热点;而在2019-2023时间段,利用U型架构(U-net,深度学习卷积神经网络的一种成熟模式)来自动测量影像学参数、多视图相关网络(MVC-Net)架构(即脊柱曲度评估框架)是主要研究热点。"
2.5 人工智能在脊柱畸形领域关键词分析结果 2.5.1 关键词共现分析 通过对关键词共现分析,能够全面展示该领域的热点和趋势[19-20]。表5总结了人工智能在脊柱畸形领域最常用的10个关键词,其中Scoliosis(脊柱侧凸)、adolescent idiopathic scoliosis(青少年特发性脊柱侧凸)分别是频次和中心性最高的关键词,说明人工智能可以高精度辅助脊柱畸形领域的手术,重点对脊柱畸形的分类与分型进行了讨论和研究,客观、真实地反映了目前人工智能在脊柱畸形领域的现状与发展趋势。图7,8显示,青少年早发性脊柱侧弯 (adolescent idiopathic scoliosis)、深度学习(deep learning)、分类(classification)、脊柱(spine)及精度(accuracy)等关键词的出现频次最高,其他的核心词都比较分散,链接的强度也较低。"
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