Kang Zhijie, Shi Guopeng, Zhu Yong, Li Feng, Li Xiaohe, Wang Haiyan
Department of Human Anatomy, Graduate School, Inner Mongolia Medical University, Hohhot, 010010, Inner Mongolia, China.
Tumor Hospital, Affiliated to Inner Mongolia Medical University, Inner Mongolia Medical University, Hohhot, 010000, Inner Mongolia, China.
J Orthop Surg Res. 2025 Jan 11;20(1):41. doi: 10.1186/s13018-024-05334-2.
The study aimed to develop a deep learning model for rapid, automated measurement of full-spine X-rays in adolescents with Adolescent Idiopathic Scoliosis (AIS). A significant challenge in this field is the time-consuming nature of manual measurements and the inter-individual variability in these measurements. To address these challenges, we utilized RTMpose deep learning technology to automate the process.
We conducted a retrospective multicenter diagnostic study using 560 full-spine sagittal plane X-ray images from five hospitals in Inner Mongolia. The model was trained and validated using 500 images, with an additional 60 images for independent external validation. We evaluated the consistency of keypoint annotations among different physicians, the accuracy of model-predicted keypoints, and the accuracy of model measurement results compared to manual measurements.
The consistency percentages of keypoint annotations among different physicians and the model were 90-97% within the 4-mm range. The model's prediction accuracies for key points were 91-100% within the 4-mm range compared to the reference standards. The model's predictions for 15 anatomical parameters showed high consistency with experienced physicians, with intraclass correlation coefficients ranging from 0.892 to 0.991. The mean absolute error for SVA was 1.16 mm, and for other parameters, it ranged from 0.22° to 3.32°. A significant challenge we faced was the variability in data formats and specifications across different hospitals, which we addressed through data augmentation techniques. The model took an average of 9.27 s to automatically measure the 15 anatomical parameters per X-ray image.
The deep learning model based on RTMpose can effectively enhance clinical efficiency by automatically measuring the sagittal plane parameters of the spine in X-rays of patients with AIS. The model's performance was found to be highly consistent with manual measurements by experienced physicians, offering a valuable tool for clinical diagnostics.
本研究旨在开发一种深度学习模型，用于快速、自动测量青少年特发性脊柱侧凸（AIS）患者的全脊柱X线片。该领域的一个重大挑战是手动测量耗时且测量结果存在个体差异。为应对这些挑战，我们利用RTMpose深度学习技术实现了测量过程的自动化。
我们进行了一项回顾性多中心诊断研究，使用了来自内蒙古五家医院的560张全脊柱矢状面X线图像。该模型使用500张图像进行训练和验证，另有60张图像用于独立的外部验证。我们评估了不同医生之间关键点标注的一致性、模型预测关键点的准确性以及与手动测量相比模型测量结果的准确性。
不同医生与模型之间关键点标注在4毫米范围内的一致性百分比为90 - 97%。与参考标准相比，模型在4毫米范围内关键点的预测准确率为91 - 100%。该模型对15个解剖参数的预测与经验丰富的医生高度一致，组内相关系数范围为0.892至0.991。SVA的平均绝对误差为1.16毫米，其他参数的平均绝对误差范围为0.22°至3.32°。我们面临的一个重大挑战是不同医院数据格式和规格的差异，我们通过数据增强技术解决了这一问题。该模型平均每张X线图像自动测量15个解剖参数耗时9.27秒。
基于RTMpose的深度学习模型可通过自动测量AIS患者X线片中脊柱的矢状面参数有效提高临床效率。研究发现该模型的性能与经验丰富的医生手动测量高度一致，为临床诊断提供了有价值的工具。
