Automated measurement of leg length discrepancy from infancy to adolescence based on cascaded LLDNet and...

Deep learning (DL) has been suggested for the automated measurement of leg length discrepancy (LLD) on radiographs, which could free up time for pediatric radiologists to focus on value-adding duties. The purpose of our study was to develop a unified solution using DL for both automated LLD measurements and comprehensive assessments in a large and comprehensive radiographic dataset covering children at all stages, from infancy to adolescence, and with a wide range of diagnoses.

The bilateral femurs and tibias were segmented by a cascaded convolutional neural network (CNN), referred to as LLDNet. Each LLDNet was conducted through use of residual blocks to learn more abundant features, a residual convolutional block attention module (Res-CBAM) to integrate both spatial and channel attention mechanisms, and an attention gate structure to alleviate the semantic gap. The leg length was calculated by localizing anatomical landmarks and computing the distances between them. A comprehensive assessment based on 9 indices (5 similarity indices and 4 stability indices) and the paired Wilcoxon signed-rank test was undertaken to demonstrate the superiority of the cascaded LLDNet for segmenting pediatric legs through comparison with alternative DL models, including ResUNet, TransUNet, and the single LLDNet. Furthermore, the consistency between the ground truth and the DL-calculated measurements of leg length was also comprehensively evaluated, based on 5 indices and a Bland-Altman analysis. The sensitivity and specificity of LLD >5 mm were also calculated.

A total of 976 children were identified (0-19 years old; male/female 522/454; 520 children between 0 and 2 years, 456 children older than 2 years, 4 children excluded). Experiments demonstrated that the proposed cascaded LLDNet achieved the best pediatric leg segmentation in both similarity indices (0.5-1% increase; P<0.05) and stability indices (13-47% percentage decrease; P<0.05) compared with the alternative DL methods. A high consistency of LLD measurements between DL and the ground truth was also observed using Bland-Altman analysis [Pearson correlation coefficient (PCC) =0.94; mean bias =0.003 cm]. The sensitivity and specificity established for LLD >5 mm were 0.792 and 0.962, respectively, while those for LLD >10 mm were 0.938 and 0.992, respectively.

The cascaded LLDNet was able to achieve promising pediatric leg segmentation and LLD measurement on radiography. A comprehensive assessment in terms of similarity, stability, and measurement consistency is essential in computer-aided LLD measurement of pediatric patients.