Lam Van Khanh, Fischer Elizabeth, Jawad Kochai, Tabaie Sean, Cleary Kevin, Anwar Syed Muhammad
Sheikh Zayed Institute for Pediatric Surgical Innovation, Childrens National Hospital, Washington, DC, 20008, USA.
School of Medicine and Health Sciences, George Washington University, Washington, DC, 20052, USA.
Int J Comput Assist Radiol Surg. 2025 Jan;20(1):203-211. doi: 10.1007/s11548-024-03254-4. Epub 2024 Sep 16.
Hip dysplasia is the second most common orthopedic condition in children with cerebral palsy (CP) and may result in disability and pain. The migration percentage (MP) is a widely used metric in hip surveillance, calculated based on an anterior-posterior pelvis radiograph. However, manual quantification of MP values using hip X-ray scans in current standard practice has challenges including being time-intensive, requiring expert knowledge, and not considering human bias. The purpose of this study is to develop a machine learning algorithm to automatically quantify MP values using a hip X-ray scan, and hence provide an assessment for severity, which then can be used for surveillance, treatment planning, and management.
X-ray scans from 210 patients were curated, pre-processed, and manually annotated at our clinical center. Several machine learning models were trained using pre-trained weights from Inception ResNet-V2, VGG-16, and VGG-19, with different strategies (pre-processing, with and without region of interest (ROI) detection, with and without data augmentation) to find an optimal model for automatic hip landmarking. The predicted landmarks were then used by our geometric algorithm to quantify the MP value for the input hip X-ray scan.
The pre-trained VGG-19 model, fine-tuned with additional custom layers, outputted the lowest mean squared error values for both train and test data, when ROI cropped images were used along with data augmentation for model training. The MP value calculated by the algorithm was compared to manual ground truth labels from our orthopedic fellows using the hip screen application for benchmarking.
The results showed the feasibility of the machine learning model in automatic hip landmark detection for reliably quantifying MP value from hip X-ray scans. The algorithm could be used as an accurate and reliable tool in orthopedic care for diagnosing, severity assessment, and hence treatment and surgical planning for hip displacement.
髋关节发育不良是脑瘫（CP）患儿第二常见的骨科疾病，可能导致残疾和疼痛。迁移百分比（MP）是髋关节监测中广泛使用的指标，基于骨盆前后位X线片计算得出。然而，在当前标准实践中，使用髋关节X线扫描手动量化MP值存在诸多挑战，包括耗时、需要专业知识以及未考虑人为偏差。本研究的目的是开发一种机器学习算法，用于使用髋关节X线扫描自动量化MP值，从而提供严重程度评估，进而用于监测、治疗计划和管理。
在我们的临床中心收集、预处理并手动标注了210例患者的X线扫描图像。使用来自Inception ResNet-V2、VGG-16和VGG-19的预训练权重，采用不同策略（预处理、有无感兴趣区域（ROI）检测、有无数据增强）训练了多个机器学习模型，以找到用于自动髋关节定位的最佳模型。然后，我们的几何算法使用预测的地标来量化输入髋关节X线扫描的MP值。
当使用ROI裁剪图像并进行数据增强用于模型训练时，经过额外自定义层微调的预训练VGG-19模型在训练数据和测试数据上均输出了最低的均方误差值。将算法计算的MP值与我们骨科医生使用髋关节筛查应用程序得出的手动地面真值标签进行比较，以进行基准测试。
结果表明机器学习模型在自动髋关节地标检测中具有可行性，可从髋关节X线扫描中可靠地量化MP值。该算法可作为骨科护理中用于诊断、严重程度评估以及髋关节移位治疗和手术规划的准确可靠工具。
