Abstract
Skeletal Bone age assessment is a routine clinical procedure carried out by paediatricians and endocrinologists for investigating a variety of endocrinological, metabolic, genetic and growth disorders in children. Skeletal maturity advances with change in structure and size of the skeletal bones with respect to age. This is commonly done by radiological investigation of the left hand due to its non dominant use. Dissent in the skeletal age and bone age values indicates abnormality. In this study, a bone-age assessment model using triplet loss for children in 0–3 years of age is proposed. Furthermore, this is the first automated bone age assessment study on lower age groups with comparable results, using one tenth of the training data samples as opposed to conventional deep neural networks. We have used small number of radiographs per class from Digital Hand Atlas Database System (DHA), a publicly available comprehensive x-ray dataset. Model trained achieves an AUC of 0.92 for binary and 0.82 for multi-class classification with visible separation in embedding clusters; thereby resulting in correct predictions on test data set.
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References
Gilsanz, V., Ratib, O.: Hand Bone Age: A Digital Atlas of Skeletal Maturity, p. 106. Springer, Heidelberg (2005). https://doi.org/10.1007/b138568
Krakow, D., Rimoin, D.L.: The skeletal dysplasias. Genet. Med. 12, 327–341 (2010)
Parnell, S., Phillips, G.: Neonatal skeletal dysplasias. Pediatr. Radiol. 42(Suppl 1), S150–S157 (2012). https://doi.org/10.1007/s00247-011-2176-2
Greulich, W.W., Pyle, S.I.: Radiographic Atlas of Skeletal Development of the Hand and Wrist. Stanford University Press, Stanford (1959)
Malina, R.M., Beunen, G.P.: Assessment of skeletal maturity and prediction of adult height (TW3 method). Am. J. Hum. Biol. 14, 788–789 (2002)
Thodberg, H.H., Kreiborg, S., Juul, A., Pedersen, K.D.: The BoneXpert method for automated determination of skeletal maturity. IEEE Trans. Med. Imaging 28(1), 52–66 (2009)
O’Connor, J.E., Coyle, J., Bogue, C., Spence, L.D., Last, J.: Age prediction formulae from radiographic assessment of skeletal maturation at the knee in an Irish population. Forensic Sci. Int. 234(188), e1–8 (2014)
Cunha, P., Moura, D.C., Guevara Lopez, M.A., Guerra, C., Pinto, D., Ramos, I.: Impact of ensemble learning in the assessment of skeletal maturity. J. Med. Syst. 38, 87 (2014). https://doi.org/10.1007/s10916-014-0087-0
Urschler, M., Grassegger, S., Stern, D.: What automated age estimation of hand and wrist MRI data tells us about skeletal maturation in male adolescents. Ann. Hum. Biol. 42(4), 358–367 (2015)
Franklin, D., Flavel, A.: CT evaluation of timing for ossification of the medial clavicular epiphysis in a contemporary Western Australian population. Int. J. Legal Med. 129(3), 583–594 (2014). https://doi.org/10.1007/s00414-014-1116-8
Pinchi, V., et al.: Combining dental and skeletal evidence in age classification: pilot study in a sample of Italian sub-adults. Leg. Med. 20, 75–9 (2016)
Hyunkwang, L., Shahein, T., Giordano, S., et al.: Fully automated deep learning system for bone age assessment. J. Digit. Imaging 30, 427–441 (2017). https://doi.org/10.1007/s10278-017-9955-8
Shi, L., Jiang, F., Ouyang, F., Zhang, J., Wang, Z., Shen, X.: DNA methylation markers in combination with skeletal and dental ages to improve age estimation in children. Forensic Sci. Int. Genet. 33, 1–9 (2018). https://doi.org/10.1016/j.fsigen.2017.11.005. PMID: 29172065
Tang, F.H., Chan, J.L.C., Chan, B.K.L.: Accurate age determination for adolescents using magnetic resonance imaging of the hand and wrist with an artificial neural network-based approach. J. Digit. Imaging 32, 283–289 (2019). https://doi.org/10.1007/s10278-018-0135-2
Ren, X., et al.: Regression convolutional neural network for automated pediatric bone age assessment from hand radiograph. IEEE J. Biomed. Health Inform. 23, 2030–2038 (2018)
Iglovikov, V.I., Rakhlin, A., Kalinin, A.A., Shvets, A.A.: Paediatric bone age assessment using deep convolutional neural networks. In: Stoyanov, D., et al. (eds.) DLMIA/ML-CDS -2018. LNCS, vol. 11045, pp. 300–308. Springer, Cham (2018). https://doi.org/10.1007/978-3-030-00889-5_34
Zhao, C., Han, J., Jia, Y., Fan, L., Gou, F.: Versatile framework for medical image processing and analysis with application to automatic bone age assessment. J. Electr. Comput. Eng. 2018, 13 (2018). Article ID 2187247
Spampinato, C., Palazzo, S., Giordano, D., et al.: Deep learning for automated skeletal bone age assessment in X-Ray images. Med. Image Anal. 36, 41–51 (2017)
Hao, P., Chokuwa, S., Xie, X., Fuli, W., Jian, W., Bai, C.: Skeletal bone age assessments for young children based on regression convolutional neural networks. Math. Biosci. Eng. 16(6), 6454–6466 (2019). https://doi.org/10.3934/mbe.2019323
Chen, M.: Automated Bone Age Classification with Deep Neural Networks (2016)
Schroff, F., Kalenichenko, D., Philbin, J.: FaceNet: a unified embedding for face recognition and clustering, pp. 815–823 (2015). arXiv:1503.03832v3
Hoffer, E., Ailon, N.: Deep metric learning using triplet network. In: Feragen, A., Pelillo, M., Loog, M. (eds.) SIMBAD 2015. LNCS, vol. 9370, pp. 84–92. Springer, Cham (2015). https://doi.org/10.1007/978-3-319-24261-3_7
Gertych, A., Zhang, A., Sayre, J., Pospiech-Kurkowska, S., Huang, H.: Bone age assessment of children using a digital hand atlas. Comput. Med. Imaging Graph.: Off. J. Comput. Med. Imaging Soc. 31(4–5), 322–331 (2007)
Zhang, A., Sayre, J.W., Vachon, L., Liu, B.J., Huang, H.K.: Racial differences in growth patterns of children assessed on the basis of bone age. Radiology 250(1), 228–235 (2009)
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Madan, S., Gandhi, T., Chaudhury, S. (2021). Bone Age Assessment for Lower Age Groups Using Triplet Network in Small Dataset of Hand X-Rays. In: Singh, M., Kang, DK., Lee, JH., Tiwary, U.S., Singh, D., Chung, WY. (eds) Intelligent Human Computer Interaction. IHCI 2020. Lecture Notes in Computer Science(), vol 12615. Springer, Cham. https://doi.org/10.1007/978-3-030-68449-5_15
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