Xie Kunjie, Zhu Suping, Lin Jincong, Li Yi, Huang Jinghui, Lei Wei, Yan Yabo
Department of Orthopedics, Xijing Hospital, Air Force Medical University, Xi'an, China.
School of Telecommunications Engineering, Xidian University, Xi'an, China.
Eur Spine J. 2025 Jul 11. doi: 10.1007/s00586-025-09106-2.
To develop an artificial intelligence (AI)-driven model for automatic Lenke classification of adolescent idiopathic scoliosis (AIS) and assess its performance.
This retrospective study utilized 860 spinal radiographs from 215 AIS patients with four views, including 161 training sets and 54 testing sets. Additionally, 1220 spinal radiographs from 610 patients with only anterior-posterior (AP) and lateral (LAT) views were collected for training. The model was designed to perform keypoint detection, pedicle segmentation, and AIS classification based on a custom classification strategy. Its performance was evaluated against the gold standard using metrics such as mean absolute difference (MAD), intraclass correlation coefficient (ICC), Bland-Altman plots, Cohen's Kappa, and the confusion matrix.
In comparison to the gold standard, the MAD for all predicted angles was 2.29°, with an excellent ICC. Bland-Altman analysis revealed minimal differences between the methods. For Lenke classification, the model exhibited exceptional consistency in curve type, lumbar modifier, and thoracic sagittal profile, with average Kappa values of 0.866, 0.845, and 0.827, respectively, and corresponding accuracy rates of 87.07%, 92.59%, and 92.59%. Subgroup analysis further confirmed the model's high consistency, with Kappa values ranging from 0.635 to 0.930, 0.672 to 0.926, and 0.815 to 0.847, and accuracy rates between 90.7 and 98.1%, 92.6-98.3%, and 92.6-98.1%, respectively.
This novel AI system facilitates the rapid and accurate automatic Lenke classification, offering potential assistance to spinal surgeons.
开发一种人工智能（AI）驱动的模型，用于青少年特发性脊柱侧凸（AIS）的Lenke自动分类，并评估其性能。
这项回顾性研究使用了来自215例AIS患者的860张脊柱X光片，包括四个视图，其中161个为训练集，54个为测试集。此外，还收集了来自610例患者的仅前后位（AP）和侧位（LAT）视图的1220张脊柱X光片用于训练。该模型旨在基于自定义分类策略进行关键点检测、椎弓根分割和AIS分类。使用平均绝对差（MAD）、组内相关系数（ICC）、Bland-Altman图、Cohen's Kappa和混淆矩阵等指标，将其性能与金标准进行比较评估。
与金标准相比，所有预测角度的MAD为2.29°，ICC极佳。Bland-Altman分析显示两种方法之间差异极小。对于Lenke分类，该模型在曲线类型、腰椎修正和胸椎矢状面轮廓方面表现出卓越的一致性，平均Kappa值分别为0.866、0.845和0.827，相应的准确率分别为87.07%、92.59%和92.59%。亚组分析进一步证实了该模型的高度一致性，Kappa值范围为0.635至0.930、0.672至0.926和0.815至0.847，准确率分别在90.7%至98.1%、92.6%至98.3%和92.6%至98.1%之间。
这种新型AI系统有助于快速、准确地进行Lenke自动分类，为脊柱外科医生提供了潜在的帮助。
