Abstract
By deep learning technique, we present a new approach to model idiopathic single curve scoliosis. We leverage the advanced version of the recurrent neural network, that is, the long short-term memory network, to achieve the goal. We frame scoliosis as a classification problem and a regression problem. A network for classification is designed first. We perform the training and testing with real clinic records that are imputed by various tricks. Using this model, one can classify the current level of scoliosis into three predefined groups via a few publicly measurable indictors, such as body height or arm span. We also design a regression network that can predict the future progression of spine curvature. This model can infer the development in spine curvature at a certain time span according to the changes of other indictors. Both of these models are evaluated by various metrics. The experiment shows that the quantitative picture of the scoliosis can be captured by our models giving a significant performance boost. Hence, the resulting decision-support system can help to decide the necessity of a further intervene both for physicians and patients.
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
References
LeCun, Y., Bengio, Y., Hinton, G.: Deep learning. Nature 521, 436–444 (2015). https://doi.org/10.1038/nature14539
Chahal, K., Dey, K.: A survey of modern object detection literature using deep learning. arXiv:1808.07256v1 (2018). http://arxiv.org/abs/1808.07256
Liu, L., et al.: Deep learning for generic object detection: a survey. arXiv:1809.02165v1 (2018). http://arxiv.org/abs/1809.02165
Hinton, G., et al.: Deep neural networks for acoustic modeling in speech recognition: the shared views of four research groups. IEEE Signal Process. Mag. 29, 82–97 (2012). https://doi.org/10.1109/MSP.2012.2205597
Sainath, T., Mohamed, A.R., Kingsbury, B., Ramabhadran, B.: Deep convolutional neural networks for LVCSR. In: Proceedings of the Acoustics, Speech and Signal Processing, pp. 8614–8618 (2013). https://doi.org/10.1109/ICASSP.2013.6639347
Ciodaro, T., Deva, D., de Seixas, J., Damazio, D.: Online particle detection with neural networks based on topological calorimetry information. J. Phys. Conf. Ser. 368, 012030 (2012). https://doi.org/10.1088/1742-6596/368/1/012030
Helmstaedter, M., et al.: Connectomic reconstruction of the inner plexiform layer in the mouse retina. Nature 500, 168–174 (2013). https://doi.org/10.1038/nature12346
Scoliosis in Depth Report. http://www.nytimes.com/health/guides/disease/scoliosis/print.html. Accessed 8 Oct 2015
Chalmers, E., et al.: Predicting success or failure of brace treatment for adolescents with idiopathic scoliosis. Med. Biol. Eng. Comput. 53, 1001–1009 (2015). https://doi.org/10.1007/s11517-015-1306-7
Deng, L.M., Hu, Y., Cheung, J.P.Y., Luk, K.D.K.: A data-driven decision support system for scoliosis prognosis. IEEE Access 5, 7874–7884 (2017). https://doi.org/10.1109/ACCESS.2017.2696704
Kuroki, H.: Brace treatment for adolescent idiopathic scoliosis. J. Clin. Med. 7, 136 (2018). https://doi.org/10.3390/jcm7060136
Wu, H., et al.: Prediction of scoliosis progression in time series using a hybrid learning technique. In: Proceedings of the 27th Annual International Conference on Engineering Medicine and Biology Society, pp. 6452–6455 (2006). https://doi.org/10.1109/IEMBS.2005.1615976
Ajemba, P.O., Ramirez, L., Durdle, N.G., Hill, D.L., Raso, V.J.: A support vectors classifier approach to predicting the risk of progression of adolescent idiopathic scoliosis. IEEE Trans. Inf. Technol. Biomed. 9, 276–282 (2005). https://doi.org/10.1109/titb.2005.847169
Hochreiter, S., Schmidhuber, J.: Long short-term memory. Neural Comput. 9, 1735–1780 (1997). https://doi.org/10.1162/neco.1997.9.8.1735
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Guo, X., Xu, S., Wang, Y., Cheung, J.P.Y., Hu, Y. (2019). Prediction Model of Scoliosis Progression Bases on Deep Learning. In: Ning, H. (eds) Cyberspace Data and Intelligence, and Cyber-Living, Syndrome, and Health. CyberDI CyberLife 2019 2019. Communications in Computer and Information Science, vol 1138. Springer, Singapore. https://doi.org/10.1007/978-981-15-1925-3_31
Download citation
DOI: https://doi.org/10.1007/978-981-15-1925-3_31
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-15-1924-6
Online ISBN: 978-981-15-1925-3
eBook Packages: Computer ScienceComputer Science (R0)