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Modernization of bone age assessment: comparing the accuracy and reliability of an artificial intelligence algorithm and shorthand bone age to Greulich and Pyle

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Abstract

Greulich and Pyle (GP) is one of the most common methods to determine bone age from hand radiographs. In recent years, new methods were developed to increase the efficiency in bone age analysis like the shorthand bone age (SBA) and automated artificial intelligence algorithms.

Objective

The aim of this study is to evaluate the accuracy and reliability of these two methods and examine if the reduction in analysis time compromises their efficacy.

Methods

Two hundred thirteen males and 213 females had their bone age determined by two separate raters using the SBA and GP methods. Three weeks later, the two raters repeated the analysis of the radiographs. The raters timed themselves using an online stopwatch. De-identified radiographs were securely uploaded to an automated algorithm developed by a group of radiologists in Toronto. The gold standard was determined to be the radiology report attached to each radiograph, written by experienced radiologists using GP.

Results

Intraclass correlation between each method and the gold standard fell within the range of 0.8–0.9, highlighting significant agreement. Most of the comparisons showed a statistically significant difference between the new methods and the gold standard; however, it may not be clinically significant as it ranges between 0.25 and 0.5 years. A bone age is considered clinically abnormal if it falls outside 2 standard deviations of the chronological age; standard deviations are calculated and provided in GP atlas.

Conclusion

The shorthand bone age method and the automated algorithm produced values that are in agreement with the gold standard while reducing analysis time.

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Authors and Affiliations

  1. Faculty of Medicine, University of British Columbia, Vancouver, Canada

    Mina Gerges & Harpreet Chhina

  2. Faculty of Science, University of British Columbia, Vancouver, Canada

    Hayley Eng

  3. Department of Orthopaedics, BC Children’s Hospital, 1D 64 4480 Oak Street, Vancouver, BC, V6H 3V4, Canada

    Harpreet Chhina & Anthony Cooper

  4. Department of Orthopaedics, Faculty of Medicine, University of British Columbia, Vancouver, Canada

    Anthony Cooper

Authors
  1. Mina Gerges
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  4. Anthony Cooper
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Corresponding author

Correspondence to Anthony Cooper.

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Conflict of interest

Dr. Mark Cicero from 16 Bit Inc. The algorithm adopted in this study is the intellectual property of 16 Bit Inc. Members of the company assisted with the use of the algorithm for the purpose of the study; however, none of the authors are affiliated with the company, nor there are any financial association with the study.

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Ethics approval was obtained from the University of British Columbia's research ethics board (H18-02756)

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Gerges, M., Eng, H., Chhina, H. et al. Modernization of bone age assessment: comparing the accuracy and reliability of an artificial intelligence algorithm and shorthand bone age to Greulich and Pyle. Skeletal Radiol 49, 1449–1457 (2020). https://doi.org/10.1007/s00256-020-03429-5

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  • DOI: https://doi.org/10.1007/s00256-020-03429-5

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