Abstract
Background
Leg length discrepancy (LLD) is a common problem that can cause long-term musculoskeletal problems. However, measuring LLD on radiography is time-consuming and labor intensive, despite being a simple task.
Objective
To develop and evaluate a deep-learning algorithm for measurement of LLD on radiographs.
Materials and methods
In this Health Insurance Portability and Accountability Act (HIPAA)-compliant retrospective study, radiographs were obtained to develop a deep-learning algorithm. The algorithm developed with two U-Net models measures LLD using the difference between the bilateral iliac crest heights. For performance evaluation of the algorithm, 300 different radiographs were collected and LLD was measured by two radiologists, the algorithm alone and the model-assisting method. Statistical analysis was performed to compare the measurement differences with the measurement results of an experienced radiologist considered as the ground truth. The time spent on each measurement was then compared.
Results
Of the 300 cases, the deep-learning model successfully delineated both iliac crests in 284. All human measurements, the deep-learning model and the model-assisting method, showed a significant correlation with ground truth measurements, while Pearson correlation coefficients and interclass correlations (ICCs) decreased in the order listed. (Pearson correlation coefficients ranged from 0.880 to 0.996 and ICCs ranged from 0.914 to 0.997.) The mean absolute errors of the human measurement, deep-learning-assisting model and deep-learning-alone model were 0.7 ± 0.6 mm, 1.1 ± 1.1 mm and 2.3 ± 5.2 mm, respectively. The reading time was 7 h and 12 min on average for human reading, while the deep-learning measurement took 7 min and 26 s. The radiologist took 74 min to complete measurements in the deep-learning mode.
Conclusion
A deep-learning U-Net model measuring the iliac crest height difference was possible on teleroentgenograms in children. LLD measurements assisted by the deep-learning algorithm saved time and labor while producing comparable results with human measurements.
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247_2022_5499_MOESM1_ESM.png
Supplementary file1 Online Supplementary Material 1 Classification of the failure cases and examples of each. Among the 300 cases, there were a total 16 failure cases (5%). Bowel gas (five cases), skin fold (four cases) and artifact (three cases) were mistakenly taken to represent the iliac crests. The iliac crest was not recognized in 4 cases. a A telegoentgenogram in a 12-year-old boy demonstrates bowel gas as iliac crest. b A Telegoentgenogram in a 14-year-old girl shows a skin fold erroneously identified as the iliac crest. c A teleroentgenogram in a 15-year-old girl demonstrates artifact erroneously identified as iliac crest. d A teleroentgenogram in a 14-year-old boy demonstrates an unrecognized iliac crest (PNG 515 KB)
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Supplementary file5 Online Supplementary Material 2 Success and failure in the deep-learning measurement. Teleroentgenograms in (a) a 15-year-old boy and (b) a 13-year-old boy. In a successful case (a), the deep-learning algorithm recognized the bilateral iliac crests and measured leg length discrepancy by calculating height difference (black arrow) between the highest points of the recognized bilateral iliac crests (white dotted line). On the other hand, in (b), the deep-learning algorithm failed to recognize correct iliac crest contour and measured height difference (white arrow) between the highest points of the recognized contour (white dotted line). The correct leg length discrepancy is the height difference between the upper white dotted line and the black dotted line (black arrow) (PNG 943 KB)
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Kim, M.J., Choi, Y.H., Lee, S.B. et al. Development and evaluation of deep-learning measurement of leg length discrepancy: bilateral iliac crest height difference measurement. Pediatr Radiol 52, 2197–2205 (2022). https://doi.org/10.1007/s00247-022-05499-0
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DOI: https://doi.org/10.1007/s00247-022-05499-0