Chu Kenneth, Kuang Xihe, Cheung Prudence W H, Li Sofia, Zhang Teng, Cheung Jason Pui Yin
Digital Health Laboratory, Department of Orthopaedics and Traumatology, The University of Hong Kong, Hong Kong SAR, China.
Conova Medical Technology Limited, Hong Kong SAR, China.
Global Spine J. 2025 Mar;15(2):770-781. doi: 10.1177/21925682231211273. Epub 2023 Oct 30.
Retrospective observational study.
The prediction of curve progression in patients with adolescent idiopathic scoliosis (AIS) remains an unresolved area in orthopedic surgery. To make a rapid meaningful prediction, easily accessible multi-dimensional data at the patient's first consultation should be used. Current studies use clinical growth parameters and numerical values extracted from radiographs to compile a predictive model, leaving out the radiographs themselves. Such practice inevitably wastes a lot of information. Thus, this study aims to create a neural network that can predict AIS progression among patients with curves indicated for bracing by integrating both one-dimensional (1D) clinical and two-dimensional (2D) radiological data collected at the patient's first visit in a fully automated manner.
513 idiopathic scoliosis patients indicated for and managed with bracing orthosis were recruited. After exclusion, 463 patients were included in deep learning analysis. Processed first-visit growth parameters and posteroanterior radiographs are used as training inputs and the curve progression outcomes obtained in follow ups are used as binary training outputs. The CapsuleNet architecture was modified and trained accordingly to make a prediction.
The final model achieved 90% sensitivity with an overall accuracy of 73.9% in the prediction of AIS in-brace curve progression by using first-visit multi-dimensional data, outperforming conventional convolutional neural networks.
This first-ever multidimensional-input model shows promise in serving as a screening tool for AIS in-brace curve progression. The incorporation of such a model into routine AIS diagnostic pipeline can assist orthopedics clinicians in personalizing the most appropriate management for each patient.
回顾性观察研究。
青少年特发性脊柱侧凸（AIS）患者曲线进展的预测在骨科手术中仍是一个未解决的领域。为了进行快速有效的预测，应使用患者首次就诊时易于获取的多维度数据。目前的研究使用临床生长参数和从X光片中提取的数值来编制预测模型，而忽略了X光片本身。这种做法不可避免地浪费了大量信息。因此，本研究旨在创建一个神经网络，通过以全自动方式整合患者首次就诊时收集的一维（1D）临床和二维（2D）放射学数据，来预测适合支具治疗的AIS患者的曲线进展。
招募了513例适合并接受支具矫形治疗的特发性脊柱侧凸患者。排除后，463例患者纳入深度学习分析。处理后的首次就诊生长参数和正位X光片用作训练输入，随访中获得的曲线进展结果用作二元训练输出。相应地修改并训练CapsuleNet架构以进行预测。
最终模型在使用首次就诊多维度数据预测AIS支具内曲线进展时，灵敏度达到90%，总体准确率为73.9%，优于传统卷积神经网络。
这个首个多维度输入模型有望作为AIS支具内曲线进展的筛查工具。将这样一个模型纳入常规AIS诊断流程可以帮助骨科临床医生为每个患者制定最适合的个性化治疗方案。
