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获得脊椎形态参数的3DU-Net神经网络架构辅助LDCT:在中国人口中进行脊椎形态综合分析

Duoshan Ma¹, Yan Wang¹, Xinxin Zhang¹

¹Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.

Calcified tissue international

|July 17, 2024

概括

一个人工智能系统通过低剂量计算机断层扫描 (LDCT) 扫描精确测量脊椎高度. 这项技术有助于分析脊椎形态及其随年龄和性别的变化,改善脊柱病理诊断.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

低剂量计算机断层扫描 (LDCT) 广泛用于肺癌查.
脊椎形态分析可以帮助诊断脊髓病态,如骨质疏松性骨折.
目前用于评估脊椎形态的方法可能耗时.

研究的目的:

评估使用人工智能系统从LDCT图像测量脊椎高度的可行性.
分析中国人口中脊椎形态与性别和年龄的相关性.
评估AI在肺癌查期间进行机会性脊椎分析的潜力.

主要方法:

开发了一个利用3D U-Net脊椎细分的AI系统.
脊椎高度 (前,中,后) 和比率 (VHRs) 从T1到L2进行测量.
人工智能测量与人工测量使用阿尔特曼和皮尔森的相关性分析进行了比较.
对不同脊椎水平,年龄组和性别的形态参数变化进行了分析.

主要成果:

基于人工智能的脊椎高度测量显示出与手动测量的高度一致性和相关性 (皮尔森r = 0.8550.919).
脊椎高度比 (VHRs) 呈现出特定的趋势,在T7/T11处出现低谷,在T9处达到峰值.
在所有年龄段,在L1和L2观察到VHR的显著性别差异.

结论:

人工智能系统可以从LDCT扫描中准确获得脊椎高度.
人工智能使得机会性脊椎形态分析成为可能,有助于诊断脊柱病理.
建议使用特定的年龄,性别和脊椎水平标准来改善形态测量评估.

关键词:

3D U-Net 神经网络是3D的神经网络.低剂量的计算机断层扫描.脊椎高度比率是指脊椎高度的比例.脊椎形态学脊椎形态学

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