Department of Radiology, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
Medical 3D Printing Center, The First Affiliated Hospital of Zhengzhou University, 1 Jianshe East Road, Zhengzhou, 450052, China.
Calcif Tissue Int. 2024 Oct;115(4):362-372. doi: 10.1007/s00223-024-01255-8. Epub 2024 Jul 17.
To evaluate the feasibility of acquiring vertebral height from chest low-dose computed tomography (LDCT) images using an artificial intelligence (AI) system based on 3D U-Net vertebral segmentation technology and the correlation and features of vertebral morphology with sex and age of the Chinese population. Patients who underwent chest LDCT between September 2020 and April 2023 were enrolled. The Altman and Pearson's correlation analyses were used to compare the correlation and consistency between the AI software and manual measurement of vertebral height. The anterior height (Ha), middle height (Hm), posterior height (Hp), and vertebral height ratios (VHRs) (Ha/Hp and Hm/Hp) were measured from T1 to L2 using an AI system. The VHR is the ratio of Ha to Hp or the ratio of Hm to Hp of the vertebrae, which can reflect the shape of the anterior wedge and biconcave vertebrae. Changes in these parameters, particularly the VHR, were analysed at different vertebral levels in different age and sex groups. The results of the AI methods were highly consistent and correlated with manual measurements. The Pearson's correlation coefficients were 0.855, 0.919, and 0.846, respectively. The trend of VHRs showed troughs at T7 and T11 and a peak at T9; however, Hm/Hp showed slight fluctuations. Regarding the VHR, significant sex differences were found at L1 and L2 in all age bands. This innovative study focuses on vertebral morphology for opportunistic analysis in the mainland Chinese population and the distribution tendency of vertebral morphology with ageing using a chest LDCT aided by an AI system based on 3D U-Net vertebral segmentation technology. The AI system demonstrates the potential to automatically perform opportunistic vertebral morphology analyses using LDCT scans obtained during lung cancer screening. We advocate the use of age-, sex-, and vertebral level-specific criteria for the morphometric evaluation of vertebral osteoporotic fractures for a more accurate diagnosis of vertebral fractures and spinal pathologies.
评估基于 3D U-Net 椎体分割技术的人工智能 (AI) 系统从胸部低剂量计算机断层扫描 (LDCT) 图像中获取椎体高度的可行性，以及中国人种的椎体形态与性别和年龄的相关性和特征。纳入 2020 年 9 月至 2023 年 4 月期间行胸部 LDCT 的患者。采用 Altman 和 Pearson 相关性分析比较 AI 软件与手动测量椎体高度的相关性和一致性。使用 AI 系统从 T1 到 L2 测量椎体的前高 (Ha)、中高 (Hm)、后高 (Hp) 和椎体高度比 (VHR)（Ha/Hp 和 Hm/Hp）。VHR 是椎体的 Ha 与 Hp 的比值或 Hm 与 Hp 的比值，可反映前楔形和双凹形椎体的形状。分析不同年龄和性别组不同椎体水平的这些参数（尤其是 VHR）的变化。AI 方法的结果高度一致且与手动测量相关。Pearson 相关系数分别为 0.855、0.919 和 0.846。VHR 呈 T7 和 T11 处的低谷和 T9 处的高峰趋势；然而，Hm/Hp 略有波动。关于 VHR，所有年龄段的 L1 和 L2 均存在显著的性别差异。本创新性研究侧重于使用基于 3D U-Net 椎体分割技术的胸部 LDCT 辅助的 AI 系统，对中国大陆人群的椎体形态进行机会性分析和随着年龄增长的椎体形态分布趋势。AI 系统具有使用肺癌筛查时获得的 LDCT 扫描自动进行机会性椎体形态分析的潜力。我们主张使用年龄、性别和椎体水平特异性标准进行椎体骨质疏松性骨折的形态计量评估，以更准确地诊断椎体骨折和脊柱病变。
