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Improving prediction of skeletal growth problems for age evaluation using hand X-rays

  • 1238: Recent Advances in Biometrics Based on Biomedical Information
  • Published:
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Abstract

Skeletal age estimation using X-ray images is a widely employed clinical method for identifying anomalies in bone growth in infants and newborns. Pediatric bone abnormalities can arise from a spectrum of conditions, including wounds, infections, or tumors. Damage to the growth plate, stemming from factors like inadequate blood supply, separation from bone components, or minor misalignment, can impede bone development, distort joint structure, and potentially result in lasting joint injuries. Divergence between chronological and assessed ages can serve as an indicator of growth-related problems, as accurate bone age assessment mirrors the actual progression of growth. Skeletal age estimation plays a pivotal role in identifying endocrine disorders, genetic abnormalities, and growth irregularities. In our effort to address the challenge of bone age assessment, this study utilizes the Radiological Society of North America’s Pediatric Bone Age Challenge dataset, comprising 12,600 radiological images of patients’ left hands, along with their gender and bone age data. We propose a robust bone age evaluation system grounded in hand skeleton guidelines for the precise detection of hand bone maturation. The proposed approach for bone age assessment centers on a tailored convolutional neural network (CNN), which attains an accuracy rate of 97%. Moreover, this research analyzes growth rate prediction using six transfer learning models, offering valuable insights into the predictive capabilities of these models. This study not only contributes to advancing bone age estimation techniques but also underscores the potential of the proposed CNN-based approach in achieving highly accurate results, further enhancing diagnostic precision in pediatric medicine.

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Data availability statement

The datasets generated during and/or analyzed during the current study are available from the corresponding author upon reasonable request.

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Acknowledgements

We would like to acknowledge the invaluable guidance and support received from our supervisors and colleagues throughout the research process. This research was funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number (PNURSP2023R349), Princess Nourah bint Abdulrahman University, Riyadh, Saudi Arabia.

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Authors and Affiliations

  1. School of Computer Science and Technology, University of Science and Technology of China (USTC), Langfang, China

    Hina Farooq

  2. Department of Computer Science, The Islamia University of Bahawalpur, Bahawalpur, Pakistan

    Muhammad Umer

  3. Department of Information Systems, College of Computer and Information Sciences, Princess Nourah bint Abdulrahman University, P.O. Box 84428, Riyadh, 11671, Saudi Arabia

    Oumaima Saidani & Latifah Almuqren

  4. Department of Computer Science, University of Salerno, Fisciano, Italy

    Riccardo Distasi

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  1. Hina Farooq
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  2. Muhammad Umer
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Correspondence to Muhammad Umer.

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Farooq, H., Umer, M., Saidani, O. et al. Improving prediction of skeletal growth problems for age evaluation using hand X-rays. Multimed Tools Appl 83, 80027–80049 (2024). https://doi.org/10.1007/s11042-023-17364-9

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