Computer-aided automatic measurement of leg length on full leg radiographs

Abstract

Objectives: To develop and evaluate a deep learning (DL)–based system for measuring leg length on full leg radiographs of diverse patients, including those with orthopedic hardware implanted for surgical treatment.

Methods: This study retrospectively assessed 2767 X-ray scanograms of 2767 patients who did or did not have orthopedic hardware implanted between January 2016 and December 2019. A cascaded DL model was developed to localize the relevant landmarks on the pelvis, knees, and ankles required for measuring leg length. Statistical analysis was performed using the correlation coefficient analysis and Bland–Altman plots to assess the agreement between the reference standard and DL-calculated lengths.

Results: Testing data comprised 400 radiographs from 400 patients. Of these radiographs, 100 were from patients with orthopedic hardware implanted in their pelvis, knees, or ankles. For all testing data, leg lengths derived from the DL-based measurement system, with or without internal fixation devices, showed excellent agreement with the reference standard (femoral length, r = 0.99 (P < .001); root mean square error (RMSE) = 0.17 cm; mean difference, − 0.01 ± 0.17 cm; 95% limit of agreement (LoA), − 0.35 to 0.34; tibial length, r = 0.99 (P < .001); RMSE = 0.17 cm; mean difference, − 0.02 ± 0.17 cm, 95% LoA, − 0.34 to 0.31; and full leg length, r = 1.0 (P < .001); RMSE = 0.19 cm; mean difference, 0.05 ± 0.18 cm; 95% LoA, − 0.31 to 0.40). The mean time for leg length measurement for each patient using the DL-based system was 8.68 ± 0.18 s.

Conclusion: The DL-based leg length measurement system could provide similar performance to radiologists in terms of accuracy and reliability for a diverse group of patients.