Abstract
This paper investigates the use of deep learning approaches to estimate the femur caput-collum-diaphyseal (CCD) angle from X-ray images. The CCD angle is an important measurement in the diagnosis of hip problems, and correct prediction can help in the planning of surgical procedures. Manual measurement of this angle, on the other hand, can be time-intensive and vulnerable to inter-observer variability. In this paper, we present a deep-learning algorithm that can reliably estimate the femur CCD angle from X-ray images. To train and test the performance of our model, we employed an X-ray image dataset with associated femur CCD angle measurements. Furthermore, we built a prototype to display the resulting predictions and to allow the user to interact with the predictions. As this is happening in a sterile setting during surgery, we expanded our interface to the possibility of being used only by voice commands. Our results show that our deep learning model predicts the femur CCD angle on X-ray images with great accuracy, with a mean absolute error of 4.3 degrees on the left femur and 4.9 degrees on the right femur on the test dataset. Our results suggest that deep learning has the potential to give a more efficient and accurate technique for predicting the femur CCD angle, which might have substantial therapeutic implications for the diagnosis and management of hip problems.
Abstract (translated)
本文研究了使用深度学习方法从X线图像中估计股骨颈干骺端（CCD）角。CCD角度是诊断髋问题的一个重要指标，正确的预测可以帮助规划手术治疗。然而，手动测量这个角度可能是耗时的，而且容易受到观察者间变异性。另一方面，深度学习可以可靠地估计股骨颈干骺端角度从X线图像中。为了训练和测试我们的模型的性能，我们使用了一个带有相应股骨颈干骺端测量值的X线图像数据集。此外，我们还构建了一个原型来显示预测结果并允许用户与预测进行交互。由于手术是在无菌环境中进行的，我们扩展了我们的界面，允许仅通过语音命令使用。我们的结果表明，我们的深度学习模型在X线图像上预测股骨颈干骺端具有很大准确性，在测试数据上的平均绝对误差为4.3度左股骨和4.9度右股骨。我们的结果表明，深度学习具有预测股骨颈干骺端角度的更高效和准确的方法的潜力，这可能对髋问题的诊断和管理产生重大影响。
URL
https://arxiv.org/abs/2404.17083
