Abstract
Adolescent idiopathic scoliosis (AIS) is a high incidence disease in adolescents, with a long treatment time and difficult to cure. As a consensus, the preliminary AIS screening is of crucial importance to detect the disease at an early stage and allows proactive interventions to prevent the disease from becoming worse and reduce future treatment. Currently, the conventional palpation or Adam forward leaning is the most widely used preliminary screening method considering the Axial Trunk Rotation (ATR) value calculated by scoliosis assessment equipment. However, this method relies heavily on the subject’s standing posture and the doctor’s experience. In this paper, we develop an efficient deep learning-based framework to enable a large-scale scoliosis screening by using only one unclothed two-dimensional (2D) human back image, without any X-radiation equipment. We classify the normal and abnormal scoliosis using ATR value as classification label which calculated from the human back three-dimensional (3D) point cloud. Our accuracy in the task of AIS classification reaches \(81.3\%\), far exceeding the accuracy of visual observation by experienced doctor (\(65\%\)), which can be used as a remote preliminary scoliosis screening method.
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
Adams, W.: Lectures on the pathology and treatment of lateral and other forms of curvature of the spine. Churchill, J. & A (1882)
Bochkovskiy, A., Wang, C.Y., Liao, H.Y.M.: Yolov4: optimal speed and accuracy of object detection. arXiv preprint arXiv:2004.10934 (2020)
Bunnell, W.P.: An objective criterion for scoliosis screening. J. Bone Joint Surgery. Am. Vol. 66(9), 1381–1387 (1984)
Chen, Q., Ge, T., Xu, Y., Zhang, Z., Yang, X., Gai, K.: Semantic human matting. In: Proceedings of the 26th ACM International Conference on Multimedia, pp. 618–626 (2018)
Church, B., Lasso, A., Schlenger, C., Borschneck, D.P., Mousavi, P., Fichtinger, G., Ungi, T.: Visualization of scoliotic spine using ultrasound-accessible skeletal landmarks. In: Medical Imaging 2017: Image-Guided Procedures, Robotic Interventions, and Modeling, vol. 10135, pp. 166–174. SPIE (2017)
Coelho, D.M., Bonagamba, G.H., Oliveira, A.S.: Scoliometer measurements of patients with idiopathic scoliosis. Braz. J. Phys. Ther. 17, 179–184 (2013)
Pan, W., Hou, G., Zhang, C.: Automatic methods for screening and assessing scoliosis by 2-d digital images. In: International Conference on Intelligent Science and Big Data Engineering, pp. 392–400. Springer (2015)
Patias, P., Grivas, T.B., Kaspiris, A., Aggouris, C., Drakoutos, E.: A review of the trunk surface metrics used as scoliosis and other deformities evaluation indices. Scoliosis 5(1), 1–20 (2010)
Pazos, V., Cheriet, F., Danserau, J., Ronsky, J., Zernicke, R.F., Labelle, H.: Reliability of trunk shape measurements based on 3-d surface reconstructions. Eur. Spine J. 16(11), 1882–1891 (2007)
Ramirez, L., Durdle, N.G., Raso, V.J., Hill, D.L.: A support vector machines classifier to assess the severity of idiopathic scoliosis from surface topography. IEEE Trans. Inf Technol. Biomed. 10(1), 84–91 (2006)
Redmon, J., Divvala, S., Girshick, R., Farhadi, A.: You only look once: unified, real-time object detection. In: Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition, pp. 779–788 (2016)
Ronneberger, O., Fischer, P., Brox, T.: U-net: Convolutional networks for biomedical image segmentation. In: International Conference on Medical Image Computing and Computer-Assisted Intervention, pp. 234–241. Springer (2015)
Tan, M., Le, Q.: Efficientnet: Rethinking model scaling for convolutional neural networks. In: International Conference on Machine Learning, pp. 6105–6114. PMLR (2019)
Turner-Smith, A.R., Harris, J.D., Houghton, G.R., Jefferson, R.J.: A method for analysis of back shape in scoliosis. J. Biomech. 21(6), 497–509 (1988)
Tzutalin, D.: Labelimg. GitHub Repository 6 (2015)
Weinstein, S.L., Dolan, L.A., Spratt, K.F., Peterson, K.K., Spoonamore, M.J., Ponseti, I.V.: Health and function of patients with untreated idiopathic scoliosis: a 50-year natural history study. JAMA 289(5), 559–567 (2003)
Xu, Z., Zhang, Y., Fu, C., Liu, L., Chen, C., Xu, W., Guo, S.: Back shape measurement and three-dimensional reconstruction of spinal shape using one kinect sensor. In: 2020 IEEE 17th International Symposium on Biomedical Imaging (ISBI), pp. 1–5. IEEE (2020)
Yan, B., Lu, X., Nie, G., Huang, Y.: China urgently needs a nationwide scoliosis screening system. Acta Paediatr. 109(11), 2416–2417 (2020)
Yang, J., Zhang, K., Fan, H., Huang, Z., Xiang, Y., Yang, J., He, L., Zhang, L., Yang, Y., Li, R., et al.: Development and validation of deep learning algorithms for scoliosis screening using back images. Commun. Biol. 2(1), 1–8 (2019)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Xu, Z. et al. (2022). 2D Photogrammetry Image of Adolescent Idiopathic Scoliosis Screening Using Deep Learning. In: Bansal, M.S., Cai, Z., Mangul, S. (eds) Bioinformatics Research and Applications. ISBRA 2022. Lecture Notes in Computer Science(), vol 13760. Springer, Cham. https://doi.org/10.1007/978-3-031-23198-8_30
Download citation
DOI: https://doi.org/10.1007/978-3-031-23198-8_30
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-23197-1
Online ISBN: 978-3-031-23198-8
eBook Packages: Computer ScienceComputer Science (R0)