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

[摘要] Background: 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. Methods: 5 mm were also calculated. Results: 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; P10 mm were 0.938 and 0.992, respectively. Conclusions: 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.

[学科分类] 外科医学

[关键词] Leg length discrepancy (LLD);deep learning (DL);radiograph