Radeaf, M., Aljobouri, H., Al-Waely, N., Algin, O. (2026). Pediatric bone age assessment with AI models based on modified Tanner-Whitehouse. , 19(1), 81-86. doi: 10.30772/qjes.2024.152727.1365
Mohammed Saadi Radeaf; Hadeel K Aljobouri; Noor Kathem Al-Waely; Oktay Algin. "Pediatric bone age assessment with AI models based on modified Tanner-Whitehouse". , 19, 1, 2026, 81-86. doi: 10.30772/qjes.2024.152727.1365
Radeaf, M., Aljobouri, H., Al-Waely, N., Algin, O. (2026). 'Pediatric bone age assessment with AI models based on modified Tanner-Whitehouse', , 19(1), pp. 81-86. doi: 10.30772/qjes.2024.152727.1365
Radeaf, M., Aljobouri, H., Al-Waely, N., Algin, O. Pediatric bone age assessment with AI models based on modified Tanner-Whitehouse. , 2026; 19(1): 81-86. doi: 10.30772/qjes.2024.152727.1365

Pediatric bone age assessment with AI models based on modified Tanner-Whitehouse

Al-Qadisiyah Journal for Engineering Sciences
Volume 19, Issue 1, March 2026, Pages 81-86    PDF (7.34 M)
Document Type: Research Paper
DOI: 10.30772/qjes.2024.152727.1365
Authors
Mohammed Saadi Radeaf* 1; Hadeel K Aljobouri1; Noor Kathem Al-Waely1; Oktay Algin2
1Department of Biomedical Engineering, College of Engineering, Al-Nahrain University, Baghdad, Iraq.
2National MR Research Center (UMRAM), Bilkent University, 06800 Ankara, Turkey.
Abstract
Assessment of bone age, which represents the development and maturity of bones. It helps treat various pediatric conditions and address legal issues. Conventional bone age assessment is a complex and laborious procedure that is susceptible to inconsistencies between different reviewers and within the same reviewer. Artificial intelligence is a new automatic, accurate, and fast method used to evaluate bone age from X-ray images. In this work, a new network design based on AI methods is proposed. This network is based on the dataset obtained from the Paediatric Bone Age Challenge organized by the Radiological Society of North America. The collection comprises 12,600 radiological pictures of left hands, each labeled with the patient's bone age and sex. The design involves two steps: first, using a Faster R-CNN mask by training the ResNet50 model to select regions of interest and then entering the selected regions into three models (Inception v3, GoogleNet, and ResNet50) for regression-based bone age estimation. The results from these models vary based on their internal structure. ResNet50 yields a mean absolute error (MAE) of 10.6 for males and 9.5 for females. Inception v3 has an MAE of 7.5 and 8.3 for Males and females, and GoogleNet has an MAE of 8.4 for males and 9.2 for females. These models can enhance the precision and effectiveness of bone age prediction.
Keywords
BAA; Hand X-ray; GoogleNet; Inception v3; ResNet50; R-CNN; TW3
References
  1. T. H. R. G. R. S. A. K. M. Mansourvar, M. A. Ismail and F. H. Nasaruddin, “Automated bone age assessment: Motivation, taxonomies, and
    challenges,” Hindawi Limited,Computational and Mathematical Methods in Medicine, vol. 2013, no. 1, p. 391626, 2013. [Online]. Available:
    https://doi.org/10.1155/2013/391626
  2.  W. R. A. Schmeling, G. Geserick and A. Olze, “Age estimation,” Forensic Sci Int, vol. 165, no. 2–3, p. 178–181, 2007. [Online]. Available:
    https://doi.org/10.1016/J.FORSCIINT.2006.05.016
  3. M. Satoh, “Bone age: assessment methods and clinical applications,” J. Clin Pediatr Endocrinol, vol. 24, no. 4, pp. 143–152, 2015. [Online]. Available:
    https://doi.org/10.1297/cpe.24.143
  4. E. M. M. S. M. Prokop Piotrkowska, K. Marszałek-Dziuba and E. Jurkiewicz, “Traditional and new methods of bone age assessment-an overview,” J Clin Res
    Pediatr Endocrinol, vol. 13, no. 3, pp. 251–262, 2021. [Online]. Available: https://doi.org/10.4274/jcrpe.galenos.2020.2020.0091
  5. Y. S. X. H. C. S. Q. X. K. Li, J. Zhang, “Automatic bone age assessment of adolescents based on weakly supervised deep convolutional neural networks,”
    IEEE Access, vol. 9, p. 120078–120087, 2021. [Online]. Available: https://doi.org/10.1109/ACCESS.2021.3108219
  6. S. Z. W. D. C. Q. X. O. D. K. X. Mao, Q. Hui, “Automated skeletal bone age assessment with two-stage convolutional transformer network based on x-ray
    images,” Diagnostics, vol. 13, no. 11, p. 1837, 2023. [Online]. Available: https://doi.org/10.3390/diagnostics13111837
  7. P. P. P. C. D. R. N. B. Poojary, P. G. Pokhare and D. J. Khanapuri, “A novel approach for bone age assessment using deep learning,” International Journal of
    Scientific Research in Computer Science Engineering and Information Technology, vol. 7, no. 3, 2021. [Online]. Available: https://doi.org/10.32628/cseit21731
  8. I. S. for Olfaction, I. S. C. Chemical Sensing, I. ofElectrical, and E. Engineers, “Isoen 2019: 18th international symposium on olfaction and electronic nose:
    2019 symposium proceedings: Acros fukuoka, may 26-29, 2019.” IEEE, Piscataway, NJ, 2019. [Online]. Available: https://doi.org/
  9. W. W. Greulich and S. I. Pyle, “Radiographic atlas of skeletal development of the hand and wrist professor of anatomy , stanford university school of
    medicine„” 2006. [Online]. Available: http://www.
  10. C.-C. Chen and Y.-X. Chou, “Bone age prediction with ai models,” International Journal of Computer Trends and Technology, vol. 71, no. 2, p. 19–24, 2022.
    [Online]. Available: https://doi.org/10.14445/22312803/ijctt-v71i2p104
  11. G. C. P. C. E. D. B. M. Vignolo, S. Milani and G. Aicardi, “Fels,greulich-pyle, and tanner-whitehouse bone age assessments in a group of italian children and
    adolescents,” American Journal of Human Biology, vol. 4, no. 4, p. 493–500, 1992. [Online]. Available: https://doi.org/10.1002/AJHB.1310040408
  12. B. D. Lee and M. S. Lee, “Automated bone age assessment using artificial intelligence: The future of bone age assessment,” Korean J Radiol, vol. 22, no. 5,
    pp. 792–800, 2021. [Online]. Available: https://doi.org/10.3348/kjr.2020.0941
  13. B. Q. X. H. C. Yang, W. Dai and W. Zhao, “A real-time automated bone age assessment system based on the rus-chn method,” Front Endocrinol (Lausanne),
    vol. 14, p. 1073219, 2023. [Online]. Available: https://doi.org/10.3389/fendo.2023.1073219
  14. M. S. N. N. B. Khalaf, H. K. Aljobouri and I. C¸ ankaya, “Simplified convolutional neural network model for automatic classification of retinal diseases
    from optical coherence tomography images,” Al-Nahrain Journal for Engineering Sciences, vol. 26, no. 4, p. 314–319, 2024. [Online]. Available:
    https://doi.org/10.29194/njes.26040314
  15. K. A. A. M. H. A. M. Umer, A. A. Eshmawi and I. Ashraf, “Skeletal age evaluation using hand x-rays to determine growth problems,” PeerJ Comput Sci,
    vol. 9, p. e1512, 2023. [Online]. Available: https://doi.org/10.7717/peerj-cs.1512
  16. F. C. F. Cavallo, A. Mohn and C. Giannini, “Evaluation of bone age in children: A mini-review,” Frontiers Media S.A., vol. 9, 2021. [Online]. Available:
    https://doi.org/10.3389/fped.2021.580314
  17. M. C. X. B. B. Liu, Y. Zhang and F. Zhou, “Bone age assessment based on rank-monotonicity enhanced ranking cnn,” IEEE Access, vol. 7, p. 120976–120983,
    2019. [Online]. Available: http://doi.org/10.1109/ACCESS.2019.2937341
  18. M. P. L. S. S. H. N. V. S. D. B. Larson, M. C. Chen and C. P. Langlotz, “Performance of a deep-learning neural network model in assessing skeletal maturity
    on pediatric hand radiographs,” Radiology, vol. 287, no. 1, p. 313–322, 2018. [Online]. Available: https://doi.org/10.1148/radiol.2017170236
  19. A. A. K. V. I. Iglovikov, A. Rakhlin and A. A. Shvets, “Paediatric bone age assessment using deep convolutional neural networks,” in Lecture Notes in
    Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), Springer Verlag, vol. 11045, p. 300–308,
    2018. [Online]. Available: https://doi.org/10.1007/978-3-030-00889-5 34
  20. X. W. J. B. Y. L. F. G. S. Z. K. C. Q. S. L. Y. Y. Eric Wu, B. Kong, “Residual attention based network for hand bone age assessment,” Computer Vision and
    Pattern Recognition, 2019. [Online]. Available: https://doi.org/10.48550/arXiv.1901.05876
  21. D. K. E. et al., “Artificial intelligence algorithm improves radiologist performance in skeletal age assessment: A prospective multicenter randomized
    controlled trial,” Radiology, vol. 301, no. 3, p. 692–699, 2021. [Online]. Available: https://doi.org/10.1148/radiol.2021204021
  22. J. He and D. Jiang, “Fully automatic model based on se-resnet for bone age assessment,” IEEE Access, vol. 9, p. 62460–62466, 2021. [Online]. Available:
    https://doi.org/10.1109/ACCESS.2021.3074713
  23. E. a. S. J. Son, Y.Song, “Tw3-based fully automated bone age assessment system using deep neural networks,” IEEE Access, vol. 7, p. 33346–33358, 2019.
    [Online]. Available: https://doi.org/10.1109/ACCESS.2019.2903131
  24. S. L. X. Z. Y. Y. S. Li, B. Liu and D. Zhang, “A deep learning-based computer-aided diagnosis method of x-ray images for bone age assessment,” Complex
    and Intelligent Systems, vol. 8, p. 1929–1939, 2022. [Online]. Available: https://doi.org/10.1007/s40747-021-00376-z
  25. T. H. M. H. M. E. J. A. H. M. Linkon, M. M. Labib, “Deep learning in prostate cancer diagnosis and gleason grading in histopathology images: An extensive
    study,” Informatics Med. Unlocked, vol. 24, no. 3, p. 100582, 2021. [Online]. Available: https://doi.org/10.1016/J.IMU.2021.100582
  26. A. BRITAL, “Inception v3 cnn architecture explained,” Medium, 2024. [Online]. Available: https://medium.com/@AnasBrital98/inception-v3-cnn-architect
    ure-explained-691cfb7bba08
  27. R. Y. Y. T. e. a. W. Li, Siqi Yu, “Machine learning model of resnet50-ensemble voting for malignant–benign small pulmonary nodule classification on
    computed tomography images,” Cancers (Basel), vol. 15, no. 22, p. 5417, 2023. [Online]. Available: https://doi.org/10.3390/cancers15225417
  28. H. A. J. M. G. W. K. L. Ali, F. Alnajjar and M. A. Serhani, “Performance evaluation of deep cnn-based crack detection and localization techniques for
    concrete structures,” Sensors, vol. 21, no. 5, p. 1688, 2021. [Online]. Available: https://doi.org/10.3390/s21051688
  29. “Googlenet cnn architecture explained (inception v1),” 2024.
  30. C. S. et al., “Going deeper with convolutions.” Computer Vision and Pattern Recognition, p. 4842, 2014. [Online]. Available: https://doi.org/10.48550/arXiv.1409.4842
Statistics
Article View: 605
PDF Download: 362


Journal Management System. Powered by iJournalPro.com