Liu Yichong, Shi Zhiliang, Xiao Chaoyang, Gao Yanzheng, Ren Hao, Wang Xinyi
School of Mechanical and Electrical Engineering, Wuhan University of Technology, Wuhan, China.
Department of Orthopedics, Henan Provincial People's Hospital, Zhengzhou, China.
Quant Imaging Med Surg. 2025 Jun 6;15(6):5204-5217. doi: 10.21037/qims-24-2138. Epub 2025 May 25.
Scoliosis, characterized by an abnormal curvature of the spine, poses significant physiological challenges during adolescent growth and development. The Cobb angle measurement is regarded as the clinical gold standard for assessing the severity of scoliosis. The measurement of the Cobb angle has been extensively investigated through advanced neural network models; however, accurately measuring the Cobb angle remains challenging due to difficulties in detecting rotational vertebral objects. Therefore, this study aimed to develop an enhanced model that improves the detection of rotational vertebrae, thereby increasing the accuracy of Cobb angle measurements for more reliable scoliosis assessment.
This study employed a redesigned YOLOv8-DSF model based on the YOLOv8n-oriented bounding box (OBB) framework, equipped with innovative modules to improve the detection of rotational objects. The method involves detecting vertebrae with rotational bounding boxes using the model and subsequently calculating the Cobb angle based on these detections. The model was tested and evaluated on a custom-built dataset that integrates private and publicly available data, all of which were enhanced through rigorous screening and augmentation processes.
The experimental findings demonstrated that the model achieved an mAP calculated with an Intersection over Union (IoU) threshold of 0.50 (mAP50) score of 0.626 and an mAP calculated with an IoU range of 0.50 to 95 (mAP50-95) score of 0.424, both exceeding the baseline by over 10%. In assessing Cobb angle accuracy, the model achieves a symmetric mean absolute percentage error (SMAPE) of 8.43 and an average mean absolute error (MAE) of 5.09 across the three Cobb angles. When tested on a public dataset for Cobb angle prediction accuracy, YOLOv8-DSF also demonstrated excellent performance.
Given that the model outperformed both the baseline and other existing methods in detecting rotational vertebral objects and calculating the Cobb angle, it contributes to more accurate scoliosis assessments. This advancement holds significant potential for clinical applications in scoliosis evaluation and management.
脊柱侧弯的特征是脊柱出现异常弯曲，在青少年生长发育过程中带来重大生理挑战。Cobb角测量被视为评估脊柱侧弯严重程度的临床金标准。通过先进的神经网络模型对Cobb角测量进行了广泛研究；然而，由于难以检测旋转的椎体目标，准确测量Cobb角仍然具有挑战性。因此，本研究旨在开发一种增强模型，改进旋转椎体的检测，从而提高Cobb角测量的准确性，以进行更可靠的脊柱侧弯评估。
本研究采用了基于面向YOLOv8n的边界框（OBB）框架重新设计的YOLOv8-DSF模型，配备了创新模块以改进旋转目标的检测。该方法包括使用模型通过旋转边界框检测椎体，随后基于这些检测结果计算Cobb角。该模型在一个定制数据集上进行测试和评估，该数据集整合了私有和公开可用数据，所有数据均通过严格的筛选和增强过程进行了强化。
实验结果表明，该模型在交并比（IoU）阈值为0.50时计算的平均精度均值（mAP）得分为0.626，在IoU范围为0.50至0.95时计算的mAP得分为0.424，两者均比基线高出10%以上。在评估Cobb角准确性时，该模型在三个Cobb角上的对称平均绝对百分比误差（SMAPE）为8.43，平均平均绝对误差（MAE）为5.09。在用于Cobb角预测准确性的公共数据集上进行测试时，YOLOv8-DSF也表现出优异的性能。
鉴于该模型在检测旋转椎体目标和计算Cobb角方面优于基线和其他现有方法，它有助于进行更准确的脊柱侧弯评估。这一进展在脊柱侧弯评估和管理的临床应用中具有巨大潜力。
