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An Automated TW3-RUS Bone Age Assessment Method with Ordinal Regression-Based Determination of Skeletal Maturity

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Abstract

The assessment of bone age is important for evaluating child development, optimizing the treatment for endocrine diseases, etc. And the well-known Tanner-Whitehouse (TW) clinical method improves the quantitative description of skeletal development based on setting up a series of distinguishable stages for each bone individually. However, the assessment is affected by rater variability, which makes the assessment result not reliable enough in clinical practice. The main goal of this work is to achieve a reliable and accurate skeletal maturity determination by proposing an automated bone age assessment method called PEARLS, which is based on the TW3-RUS system (analysis of the radius, ulna, phalanges, and metacarpal bones). The proposed method comprises the point estimation of anchor (PEA) module for accurately localizing specific bones, the ranking learning (RL) module for producing a continuous stage representation of each bone by encoding the ordinal relationship between stage labels into the learning process, and the scoring (S) module for outputting the bone age directly based on two standard transform curves. The development of each module in PEARLS is based on different datasets. Finally, corresponding results are presented to evaluate the system performance in localizing specific bones, determining the skeletal maturity stage, and assessing the bone age. The mean average precision of point estimation is 86.29%, the average stage determination precision is 97.33% overall bones, and the average bone age assessment accuracy is 96.8% within 1 year for the female and male cohorts.

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Availability of Data and Material

The dataset employed in this paper is private, and it is not available for now.

Code Availability

All code was implemented in Python. Both the code and scripts to reproduce the experiments of this paper will be available soon.

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Funding

This work was supported in part by the National Natural Science Foundation of China (62003284) and National Science and The Presidential Research Foundation of Xiamen University (NO. 20720210089).

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Authors and Affiliations

  1. State Key Laboratory of Molecular Vaccinology and Molecular Diagnostics, School of Public Health, Xiamen University, Xiamen, Fujian, 361000, China

    Dongxu Zhang, Bowen Liu, Yulin Huang, Yang Yan, Jun Zhang & Ningshao Xia

  2. Department of Pediatrics, Zhangzhou Affiliated Hospital of Fujian Medical University, Zhangzhou, Fujian, 363000, China

    Shaowei Li, Jinshui He & Shuyun Zhang

Authors
  1. Dongxu Zhang
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  2. Bowen Liu
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  3. Yulin Huang
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  4. Yang Yan
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Zhang, D., Liu, B., Huang, Y. et al. An Automated TW3-RUS Bone Age Assessment Method with Ordinal Regression-Based Determination of Skeletal Maturity. J Digit Imaging 36, 1001–1015 (2023). https://doi.org/10.1007/s10278-023-00794-0

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