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A deep learning approach to automatically quantify lower extremity alignment in children

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Abstract

Objective

To develop and validate a convolutional neural network (CNN) capable of predicting the anatomical landmarks used to calculate the hip-knee-ankle angles (HKAAs) from radiographs and thereby quantify lower extremity alignments in children.

Materials and methods

A search of the image archive at a large children’s hospital was conducted to identify full-length lower extremity radiographs performed in children (≤ 18 years old) for the indication of lower extremity alignment (7/2019–10/2019). A radiologist manually labeled each radiograph’s six requisite anatomical landmarks used to measure HKAAs (bilateral centers of the femoral head, tibial spine, and tibial plafond) and defined the resultant labels as ground truth. A 2D heatmap was generated for each ground truth landmark to encode the pseudo-probability of a landmark being at a particular location. A CNN was developed for indirect landmark localization by regressing across a collection of these heatmaps. The landmarks predicted from this model were used to calculate the HKAAs. Absolute prediction error and intraclass correlation were used to assess the accuracy of the HKAA estimates.

Results

The study cohort consisted of 528 radiographs from 517 patients (mean age = 10.8 years, SD = 4.2 years). Evaluation of this CNN showed few HKAA prediction outliers (12/1056 [1.1%]), defined as having an absolute prediction error of > 10°. Excluding these outliers, the study cohort’s mean absolute prediction error for the HKAA was 0.94° ± 0.84°, and the intraclass correlation between the ground truth and prediction was 0.974.

Conclusion

The proposed CNN generated promising results and offers potential for using this model as a computer-aided diagnostic tool.

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Acknowledgements

The author would like to thank Ms. Nancy Drinan for her help in the editing of the manuscript. The author would also like to acknowledge the use of Boston Children’s Hospital's HighPerformance Computing Resources BCH HPC Cluster Enkefalos 2 (E2) which has been crucial to the research reported in this publication. The software used in the project was installed and configured by BioGrids.

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Authors and Affiliations

  1. Department of Radiology, Boston Children’s Hospital, Harvard Medical School, 300 Longwood Avenue, Boston, MA, 02115, USA

    Andy Tsai

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  1. Andy Tsai
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Correspondence to Andy Tsai.

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Ethics approval

Approval was obtained by the local institutional review board at Boston Children’s Hospital, Harvard Medical School in Boston, Massachusetts, USA (protocol number IRB- P00037876).

Informed consent

Written informed consent was waived by the institutional review board at Boston Children’s Hospital.

Conflict of interest

The author declares no competing interests.

Study subjects or cohorts overlap

The study subjects and cohorts have not been previously reported.

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The scientific guarantor of this publication is Andy Tsai. He agrees to be accountable for all aspects of this work.

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Tsai, A. A deep learning approach to automatically quantify lower extremity alignment in children. Skeletal Radiol 51, 381–390 (2022). https://doi.org/10.1007/s00256-021-03844-2

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  • DOI: https://doi.org/10.1007/s00256-021-03844-2

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