Kang Dong-Ho, Jeong Ye-Jin, Kim Sung Taeck, Kim Younguk, Chang Bong-Soon, Kim Hyoungmin, Chang Sam Yeol, Ro Du Hyun
Department of Orthopedic Surgery, Samsung Medical Center, Seoul, Republic of Korea; College of Medicine, Seoul National University, Seoul, Republic of Korea.
College of Mathematics, Korea University, Seoul, Republic of Korea; Research & Development Department, CONNECTEVE Co., Ltd, Seoul, Republic of Korea.
Spine J. 2025 Aug;25(8):1688-1697. doi: 10.1016/j.spinee.2025.01.020. Epub 2025 Jan 31.
Accurate and consistent measurement of sagittal alignment is challenging, particularly in patients with severe coronal deformities, including degenerative lumbar scoliosis (DLS).
This study aimed to develop and validate an artificial intelligence (AI)-based system for automating the measurement of key sagittal parameters, including lumbar lordosis, pelvic incidence, pelvic tilt, and sacral slope, with a focus on its applicability across a wide range of deformities, including severe coronal deformities, such as DLS.
Retrospective observational study.
A total of 1,011 standing lumbar lateral radiographs, including DLS.
Interclass and intraclass correlation coefficients (CC), and Bland-Altman plots.
The model utilizes a deep-learning framework, incorporating a U-Net for segmentation and a Keypoint Region-based Convolutional Neural Network for keypoint detection. The ground truth masks were annotated by an experienced orthopedic specialist. The performance of the model was evaluated against ground truth measurements and assessments from two expert raters using interclass and intraclass CC, and Bland-Altman plots.
In the test set of 113 patients, 39 (34.5%) had DLS, with a mean Cobb's angle of 14.8°±4.4°. The AI model achieved an intraclass CC of 1.00 across all parameters, indicating perfect consistency. Interclass CCs comparing the AI model to ground truth ranged from 0.96 to 0.99, outperforming experienced orthopedic surgeons. Bland-Altman analysis revealed no significant systemic bias, with most differences falling within clinically acceptable ranges. A 5-fold cross-validation further demonstrated robust performance, with interclass CCs ranging from 0.96 to 0.99 across diverse subsets.
This AI-based system offers a reliable and efficient automated measurement of sagittal parameters in spinal deformities, including severe coronal deformities. The superior performance of the model compared with that of expert raters highlights its potential for clinical applications.
准确且一致地测量矢状面排列具有挑战性，尤其是在患有严重冠状面畸形的患者中，包括退变性腰椎侧凸（DLS）。
本研究旨在开发并验证一种基于人工智能（AI）的系统，用于自动测量关键矢状面参数，包括腰椎前凸、骨盆入射角、骨盆倾斜度和骶骨斜率，重点关注其在包括严重冠状面畸形（如DLS）在内的广泛畸形中的适用性。
回顾性观察研究。
总共1011张腰椎站立位侧位X线片，包括DLS患者的片子。
组间和组内相关系数（CC）以及Bland-Altman图。
该模型采用深度学习框架，包括用于分割的U-Net和用于关键点检测的基于关键点区域的卷积神经网络。真实掩膜由一位经验丰富的骨科专家标注。使用组间和组内CC以及Bland-Altman图，将模型的性能与真实测量值以及两位专家评估者的评估结果进行比较。
在113例患者的测试集中，39例（34.5%）患有DLS，平均Cobb角为14.8°±4.4°。AI模型在所有参数上的组内CC均达到1.00，表明具有完美的一致性。将AI模型与真实值进行比较的组间CC范围为0.96至0.99，优于经验丰富的骨科医生。Bland-Altman分析显示无明显系统偏差，大多数差异落在临床可接受范围内。五折交叉验证进一步证明了该模型的稳健性能，在不同子集中组间CC范围为0.96至0.99。
这种基于AI的系统为脊柱畸形（包括严重冠状面畸形）的矢状面参数提供了可靠且高效的自动测量。该模型与专家评估者相比的卓越性能突出了其在临床应用中的潜力。
