Abstract
Skeletal age assessment (SAA) is one of the techniques to verify the claimed age and actual age. It is also performed to evaluate whether skeletal age is normal or delayed compared to the patient’s chronological age (CA). A delayed or advanced bone age can indicate growth disorders. It is generally performed by using either the Greulich & Pyle (G&P) method or the Tanner–Whitehouse (TW) method. However, inter- and intra-observer differences occur that could be resolved by developing an automatic system. Three years back (in 2017) Radiological Society of North America (RSNA) challenged the data science community to leverage computer vision to predict SA more efficiently and accurately than existing methods. The primary goal of this research work is to use convolutional neural networks to build a model to predict skeletal age with high accuracy given RSNA’s provided dataset. The secondary goal is to leverage deep learning visualization techniques for better interpretation of our results. Overall, our proposed model achieved a competitive MAE of 7.61 months on the test set provided by Radiological Society of North America (RSNA).
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Similar content being viewed by others
References
A. Kinnikar, M. Husain, S.M. Meena, Face recognition using gabor filter and convolutional neural network. Paper presented at the ACM International Conference Proceeding Series, 25–26 Aug 2016. https://doi.org/10.1145/2980258.2982104
V.J. Rathod, N.C. Iyer, S.M. Meena, A survey on fingerprint biometric recognition system. Paper presented at the Proceedings of the 2015 International Conference on Green Computing and Internet of Things, ICGCIoT 2015, pp. 323–326 (2016). https://doi.org/10.1109/ICGCIoT.2015.7380482
K.U. Sreekanth, S. Shetty, S.M. Meena, Head pose estimation using transfer learning. Paper presented at the Proceedings of the 2018 International Conference on Recent Trends in Advanced Computing, ICRTAC-CPS 2018, pp. 73–79 (2019). https://doi.org/10.1109/ICRTAC.2018.8679209
G.P. Mallya, A. Hiremani, G. Gajakosh, D. Badiger, M.P.S. Hiremath, Framework for automatic diagnosis of psychological disorders, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (IEEE, 2018), pp. 929–934
S.G. Prateek, J. Jagadeesh, R. Siddarth, Y. Smitha, P.S. Hiremath, N.T. Pendari, Dynamic tool for American sign language finger spelling interpreter, in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN) (IEEE, 2018), pp. 596–600
E. Pietka, M.F. McNitt-Gray, M.L. Kuo, H.K. Huang, Computer-assisted phalangeal analysis in skeletal age assessment. IEEE Trans. Med. Imaging 10(4), 616–620 (1991)
X. Pan, et al., Fully automated bone age assessment on large-scale hand X-ray dataset. Int. J. Biomed. Imaging (2020)
V.I. Iglovikov, A. Rakhlin, A.A. Kalinin, A.A. Shvets, Paediatric bone age assessment using deep convolutional neural networks, in Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support, ed. by D. Stoyanov (Springer, 2018), pp. 300–308
D.B. Larson, et al., Performance of a deep-learning neural network model in assessing skeletal maturity on pediatric hand radiographs. Radiology 287(1) (2017)
C. Spampinato, et al., Deep learning for automated skeletal bone age assessment in X-ray images. Med. Image Anal. 36, 41–51 (2017)
H. Lee, et al., Fully automated deep learning system for bone age assessment. J. Digital Imag. 30(4) 427–441 (2017)
V. De Sanctis, S. Di Maio, A.T. Soliman, G. Raiola, R. Elalaily, G. Millimaggi, Hand X-ray in pediatric endocrinology: skeletal age assessment and beyond. Indian J. Endocrinol. Metab. 18(Suppl 1), S63 (2014)
K. Simonyan, A. Zisserman, Very deep convolutional networks for large-scale image recognition. arXiv preprint arXiv:1409.1556 (2014).
E. Wu, B. Kong, X. Wang et al., Residual attention based network for hand bone age assessment, in 2019 IEEE 16th International Symposium on Biomedical Imaging (ISBI 2019), pp. 1158–1161, Venice, Italy, Italy (2019)
J. Han, Y. Jia, C. Zhao, F. Gou, Automatic bone age assessment combined with transfer learning and support vector regression, in 2018 9th International Conference on Information Technology in Medicine and Education (ITME), pp. 61–66, Hangzhou, China (2018)
S.H. Tajmir, H. Lee, R. Shailam, et al., Artificial intelligence assisted interpretation of bone age radiographs improves accuracy and decreases variability. Skeletal Radiol. 48(2), 275–283 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2021 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Desai, S.D., Kadiyavar, P.T. (2021). Pediatric Skeletal Age Assessment Using Deep Learning Proceedings. In: Bhateja, V., Satapathy, S.C., Travieso-González, C.M., Aradhya, V.N.M. (eds) Data Engineering and Intelligent Computing. Advances in Intelligent Systems and Computing, vol 1407. Springer, Singapore. https://doi.org/10.1007/978-981-16-0171-2_12
Download citation
DOI: https://doi.org/10.1007/978-981-16-0171-2_12
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-16-0170-5
Online ISBN: 978-981-16-0171-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)