Liu Jinlong, Zhang Haoran, Dong Pei, Su Danyang, Bai Zhen, Ma Yuanbo, Miao Qiuju, Yang Shenyu, Wang Shuaikun, Yang Xiaopeng
Department of Radiology, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
Department of Medical Equipment, The First Affiliated Hospital of Zhengzhou University, Zhengzhou, Henan, China.
J Orthop Surg Res. 2025 Jan 3;20(1):9. doi: 10.1186/s13018-024-05383-7.
Adolescent idiopathic scoliosis (AIS) is a complex three-dimensional deformity, and up to now, there has been no literature reporting the analysis of a large sample of X-ray imaging parameters based on artificial intelligence (AI) for it. This study is based on the accurate and rapid measurement of x-ray coronal imaging parameters in AIS patients by AI, to explore the differences and correlations, and to further investigate the risk factors in different groups, so as to provide a theoretical basis for the diagnosis and surgical treatment of AIS.
Retrospective analysis of 3192 patients aged 8-18 years who had a full-length orthopantomogram of the spine and were diagnosed with AIS at the First Affiliated Hospital of Zhengzhou University from January 2019 to March 2024. After screened 2092 cases were finally included. The uAI DR scoliosis analysis system with multi-resolution VB-Net convolution network architecture was used to measure CA, CBD, CV, RSH, T1 Tilt, PT, LLD, SS, AVT, and TS parameters. The results were organized and analyzed by using R Studio 4.2.3 software.
The differences in CA, CBD, CV, RSH, TI tilt, PT, LLD and SS were statistically significant between male and female genders (p < 0.05); Differences in CA, CBD, T1 Tilt, PT, SS, AVT and TS were statistically significant in patients with AIS of different severity (p < 0.001), and T1 Tilt, AVT, TS were risk factors; Differences in CA, CBD, CV, RSH, T1 Tilt, PT, LLD, SS, AVT and TS were statistically significant (p < 0.05) in patients with AIS of different curve types, and TS was a risk factor; Analyzing the correlation between parameters revealed a highly linear correlation between CV and RSH (r = 0.826, p < 0.001), and a significant linear correlation between CBD and TS, and PT and SS (r = 0.561, p < 0.001; r = 0.637, p < 0.001).
Measurements based on VB-Net neural network found that x-ray coronal imaging parameters varied among AIS patients with different curve types and severities. In clinical practice, it is recommended to consider the discrepancy in parameters to enable a more accurate diagnosis and a personalized treatment plan.
青少年特发性脊柱侧凸（AIS）是一种复杂的三维畸形，截至目前，尚无文献报道基于人工智能（AI）对大量X线影像参数进行分析。本研究基于AI对AIS患者X线冠状位影像参数进行准确、快速测量，探索其差异及相关性，并进一步研究不同组别的危险因素，为AIS的诊断及手术治疗提供理论依据。
回顾性分析2019年1月至2024年3月在郑州大学第一附属医院行全脊柱正位曲面断层摄影且年龄在8-18岁、诊断为AIS的3192例患者。经筛选后最终纳入2092例。采用具有多分辨率VB-Net卷积网络架构的uAI DR脊柱侧凸分析系统测量冠状面 Cobb角（CA）、椎体中心偏移距离（CBD）、椎体旋转度（CV）、肋骨-椎体矢状面距离（RSH）、T1倾斜角（T1 Tilt）、骨盆倾斜角（PT）、双下肢不等长（LLD）、骶骨倾斜角（SS）、顶椎偏移距离（AVT）和顶椎旋转度（TS）参数。利用R Studio 4.2.3软件对结果进行整理和分析。
CA、CBD、CV、RSH、T1倾斜角、PT、LLD和SS在男女性别间差异有统计学意义（p＜0.05）；CA、CBD、T1倾斜角、PT、SS、AVT和TS在不同严重程度AIS患者中差异有统计学意义（p＜0.001），且T1倾斜角、AVT、TS为危险因素；CA、CBD、CV、RSH、T1倾斜角、PT、LLD、SS、AVT和TS在不同弯型AIS患者中差异有统计学意义（p＜0.05），且TS为危险因素；参数相关性分析显示CV与RSH呈高度线性相关（r = 0.826，p＜0.001），CBD与TS、PT与SS呈显著线性相关（r = 0.561，p＜0.001；r = 0.637，p＜0.001）。
基于VB-Net神经网络测量发现，不同弯型和严重程度的AIS患者X线冠状位影像参数存在差异。在临床实践中，建议考虑参数差异以实现更准确的诊断和个性化治疗方案。
