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在腰椎X射线图像中预测骨质疏松症,使用多尺度加权融合上下文变压器网络

Linyan Xue¹, Geng Qin², Shilong Chang²

¹College of Quality and Technical Supervision, Hebei University, Baoding 071002, China; Hebei Technology Innovation Center for Lightweight of New Energy Vehicle Power System, Baoding 071002, China; National & Local Joint Engineering Research Center of Metrology Instrument and System, Hebei University, Baoding 071002, China.

Artificial intelligence in medicine

|September 6, 2023

概括

一个新的深度学习模型,FCoTNet,通过改进纹理和局部特征融合来增强X射线图像的骨质疏松症诊断. 这种人工智能工具显著提高了临床医生的骨质疏松症检测准确性和一致性,有助于预防骨折.

科学领域:

医疗成像医学成像
人工智能的人工智能
整形外科整形外科整形外科

背景情况:

骨质疏松症的特点是骨密度降低和骨折风险增加,这对健康构成重大挑战.
像DXA这样的当前诊断方法昂贵,自动化X射线分析受到图像噪声和低对比度的阻碍.
深度学习提供了一个低成本的替代品,用于从X光片评估骨质疏松症.

研究的目的:

开发一个先进的深度学习网络,FCoTNet,以从X射线图像中改进骨质疏松症诊断.
解决传统变压器网络中存在的纹理和局部特征融合的局限性.
为了提高自动骨质疏松症检测的准确性和可靠性.

主要方法:

提出了一个端到端的变压器式网络,FCoTNet,集成并行的多尺度特征提取.
在FCoTNet中使用Convolution来汇总每个尺度上的不同受体场的特征.
较大的聚变重量被分配给小受体场特征图,以优先考虑诊断的关键小规模纹理特征.

主要成果:

在一个私人腰椎X射线数据集上,FCoTNet实现了多分类的平均准确率为78.29%,灵敏度为69.72%,特异性为88.92%.
在一项受控试验中,使用FCoTNet时,临床医生的准确性提高了18.50% (辅助与无辅助),显著提高了灵敏度和特异性.
FCoTNet在外部测试数据集上显示出稳定性,并改善了临床医生之间的预测一致性.

结论:

拟议的FCoTNet深度学习模型实现了从X射线图中预测骨质疏松症的最先进性能.
FCoTNet 显著提高了骨质疏松症查准确度,并有助于减少骨质疏松症相关骨折.
该模型提供了一个临床上有价值的工具,增强了骨科临床医生的诊断能力.

关键词:

腰椎X射线脊椎X射线脊椎X射线多尺度加权聚变多尺度加权聚变骨质疏松症是一种骨质疏松症.变压器式的风格.

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