Automated measurements of leg length on radiographs by deep learning

Abstract

Deep learning algorithms can evaluate large and complex sets of data, offering various support for medical imaging analysis. Previous works have explored applications of deep learning to measure leg lengths more efficiently. These previous studies provide evidence to suggest deep-learning algorithms can improve efficiency with high levels of accuracy and speed. In this retrospective study, we utilize deep learning-based convolutional neural networks, programmed with input from a human expert, to identify key points and measure leg length. We collected frontal computed tomography (CT) scout radiographs from pre-operative CT scans of patients undergoing evaluation for knee arthroplasty from diverse sources to both train and test the model. We prepared a DenseNet121 model to predict and identify key points, which were then used to develop patch-based models. We applied separable convolutional layers to complete the analysis. The data reflects that 1) separable convolution exhibits lower mean absolute error (MAE) and increased convergence speed as compared to global average pooling layers and 2) optimal learning rates, batch size, and patch size can be achieved to present the least MAE. Our findings provide useful information and an automated tool to assist radiologists to diagnose leg length discrepancy in clinical practice.