Kim Jeoung Kun, Wang Ming Xing, Park Donghwi, Chang Min Cheol
Department of Business Administration, School of Business, Yeungnam University, Gyeongsan-si, Republic of Korea.
College of Economics and Management, Wenzhou University of Technology, Wenzhou, Zhejiang, China.
Sci Rep. 2025 Jul 22;15(1):26647. doi: 10.1038/s41598-025-11295-1.
Accurate assessments of axial vertebral rotation (AVR) is essential for managing idiopathic scoliosis. The Nash-Moe classification method has been extensively used for AVR assessment; however, its subjective nature can lead to measurement variability. Therefore, herein, we propose an automated deep learning (DL) model for AVR assessment based on posteroanterior spinal radiographs. We develop a two-stage DL framework using the MMRotate toolbox and analyze 1080 posteroanterior spinal radiographs of patients aged 4-18 years. The framework comprises a vertebra detection model (864 training and 216 validation images) and a pedicle detection model (14,608 training and 3652 validation images). We improved the Nash-Moe classification method by implementing a 12-segment division system and width ratio metric for precise pedicle assessment. The vertebra and pedicle detection models achieved mean average precision values of 0.909 and 0.905, respectively. The overall classification accuracy was 0.74, with grade-specific performance between 0.70 and 1.00 for precision and 0.33 and 0.93 for recall across Grades 0-3. The proposed DL framework processed complete posteroanterior radiographs in < 5 s per case compared with conventional manual measurements (114 s per radiograph). The best performance was observed in mild to moderate rotation cases, with performance in severe rotation cases limited by insufficient data. The implementation of DL framework for the automated Nash-Moe classification method exhibited satisfactory accuracy and exceptional efficiency. However, this study is limited by low recall (0.33) for Grade 3 and the inability to classify Grade 4 towing to dataset constraints. Further validation using augmented datasets that include severe rotation cases is necessary.
准确评估椎体轴向旋转（AVR）对于特发性脊柱侧弯的治疗至关重要。Nash-Moe分类方法已被广泛用于AVR评估；然而，其主观性可能导致测量变异性。因此，在本文中，我们提出了一种基于脊柱后前位X线片的用于AVR评估的自动化深度学习（DL）模型。我们使用MMRotate工具箱开发了一个两阶段的DL框架，并分析了1080例4至18岁患者的脊柱后前位X线片。该框架包括一个椎体检测模型（864张训练图像和216张验证图像）和一个椎弓根检测模型（14608张训练图像和3652张验证图像）。我们通过实施一个12段划分系统和宽度比度量来改进Nash-Moe分类方法，以进行精确的椎弓根评估。椎体和椎弓根检测模型的平均精度值分别为0.909和0.905。总体分类准确率为0.74，0至3级的特定等级性能在精度方面为0.70至1.00，召回率方面为0.33至0.93。与传统手动测量（每张X线片114秒）相比，所提出的DL框架处理完整的脊柱后前位X线片的时间<5秒/例。在轻度至中度旋转病例中观察到最佳性能，重度旋转病例的性能受数据不足限制。用于自动化Nash-Moe分类方法的DL框架的实施表现出令人满意的准确性和卓越的效率。然而，本研究受到3级召回率低（0.33）以及由于数据集限制无法对4级进行分类的限制。有必要使用包括重度旋转病例的增强数据集进行进一步验证。
