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Open Access

Deep Learning Blockchain Integration Framework for Ureteropelvic Junction Obstruction Diagnosis Using Ultrasound Images

Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Beijing Children’s Hospital Affiliated to Capital Medical University, Beijing 100020, China
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UreteroPelvic Junction Obstruction (UPJO) is a common hydronephrosis disease in children that can result in an even progressive loss of renal function. Ultrasonography is an economical, radiationless, noninvasive, and high noise preliminary diagnostic step for UPJO. Artificial intelligence has been widely applied to medical fields and can greatly assist doctors’ diagnostic abilities. The demand for a highly secure network environment in transferring electronic medical data online, therefore, has led to the development of blockchain technology. In this study, we built and tested a framework that integrates a deep learning diagnosis model with blockchain technology. Our diagnosis model is a combination of an attention-based pyramid semantic segmentation network and a discrete wavelet transformation-processed residual classification network. We also compared the performance between benchmark models and our models. Our diagnosis model outperformed benchmarks on the segmentation task and classification task with MIoU = 87.93, MPA = 93.52, and accuracy = 91.77%. For the blockchain system, we applied the InterPlanetary File System protocol to build a secure and private sharing environment. This framework can automatically grade the severity of UPJO using ultrasound images, guarantee secure medical data sharing, assist in doctors’ diagnostic ability, relieve patients’ burden, and provide technical support for future federated learning and linkage of the Internet of Medical Things (IoMT).


M. J. Siegel, Pediatric Sonography, 4th ed. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2011.
J. M. Smith, D. M. Stablein, R. Munoz, D. Hebert, and R. A. McDonald, Contributions of the transplant registry: The 2006 annual report of the North American Pediatric Renal Trials and Collaborative Studies (NAPRTCS), Pediatr. Transplant., vol. 11, no. 4, pp. 366–373, 2007.
B. A. Warady and V. Chadha, Chronic kidney disease in children: The global perspective, Pediatr. Nephrol., vol. 22, no. 12, pp. 1999–2009, 2007.
H. T. Nguyen, C. D. A. Herndon, C. Cooper, J. Gatti, A. Kirsch, P. Kokorowski, R. Lee, M. Perez-Brayfield, P. Metcalfe, and E. Yerkes, et al., The society for fetal urology consensus statement on the evaluation and management of antenatal hydronephrosis, J. Pediatr. Urol., vol. 6, no. 3, pp. 212–231, 2010.
H. Hashim and C. R. J. Woodhouse, Ureteropelvic junction obstruction, Eur. Urol. Suppl., vol. 11, no. 2, pp. 25–32, 2012.
A. K. Ucar and S. Kurugoglu, Urinary ultrasound and other imaging for ureteropelvic junction type hydronephrosis (UPJHN), Front. Pediatr., vol. 8, p. 546, 2020.
American Institute of Ultrasound in Medicine, AIUM practice guideline for the performance of an ultrasound examination in the practice of urology, J. Ultrasound Med., vol. 31, no. 1, pp. 133–144, 2012.
American Institute of Ultrasound in Medicine, AIUM practice guideline for the performance of an ultrasound examination in the practice of urology, J. Ultrasound Med., vol. 31, no. 1, pp. 133–144, 2012.
L. C. Smail, K. Dhindsa, L. H. Braga, S. Becker, and R. R. Sonnadara, Using deep learning algorithms to grade hydronephrosis severity: Toward a clinical adjunct, Front. Pediatr., vol. 8, p. 1, 2020.
S. Turco, P. Frinking, R. Wildeboer, M. Arditi, H. Wijkstra, J. R. Lindner, and M. Mischi, Contrast-enhanced ultrasound quantification: From kinetic modeling to machine learning, Ultrasound Med. Biol., vol. 46, no. 3, pp. 518–543, 2020.
H. Shokoohi, M. A. Lesaux, Y. H. Roohani, A. Liteplo, C. Huang, and M. Blaivas, Enhanced point-of-care ultrasound applications by integrating automated feature-learning systems using deep learning, J. Ultrasound Med., vol. 38, no. 7, pp. 1887–1897, 2019.
K. Dhindsa, L. C. Smail, M. McGrath, L. H. Braga, S. Becker, and R. R. Sonnadara, Grading prenatal hydronephrosis from ultrasound imaging using deep convolutional neural networks, in Proc. 15th Conf. on Computer and Robot Vision (CRV), Toronto, Canada, 2018, pp. 80–87.
E. S. Blum, A. R. Porras, E. Biggs, P. R. Tabrizi, R. D. Sussman, B. M. Sprague, E. Shalaby-Rana, M. Majd, H. G. Pohl, and M. G. Linguraru, Early detection of ureteropelvic junction obstruction using signal analysis and machine learning: A dynamic solution to a dynamic problem, J. Urol., vol. 199, no. 3, pp. 847–852, 2018.
J. X. He, S. L. Baxter, J. M. Xu, X. T. Zhou, and K. Zhang, The practical implementation of artificial intelligence technologies in medicine, Nat. Med., vol. 25, no. 1, pp. 30–36, 2019.
M. Chen, X. B. Shi, Y. Zhang, D. Wu, and M. Guizani, Deep feature learning for medical image analysis with convolutional autoencoder neural network, IEEE Trans. Big Data, vol. 7, no. 4, pp. 750–758, 2021.
R. Hillestad, J. Bigelow, A. Bower, F. Girosi, R. Meili, R. Scoville, and R. Taylor, Can electronic medical record systems transform health care? Potential health benefits, savings, and costs, Health Affairs, vol. 24, no. 5, pp. 1103–1117, 2005.
P. Vimalachandran, H. Wang, and Y. C. Zhang, Securing electronic medical record and electronic health record systems through an improved access control, in Proc. 4th Int. Conf. on Health Information Science, Melbourne, Australia, 2015, pp. 17–30.
S. Biswas, K. Sharif, F. Li, I. Alam, and S. Mohanty, DAAC: Digital asset access control in a unified blockchain based E-health system, IEEE Trans. Big Data, .
C. J. McDonald, The barriers to electronic medical record systems and how to overcome them, J. Am. Med. Inf. Assoc., vol. 4, no. 3, pp. 213–221, 1997.
S. Selvaraj and S. Sundaravaradhan, Challenges and opportunities in IoT healthcare systems: A systematic review, SN Appl. Sci., vol. 2, no. 1, p. 139, 2020.
S. Rahmadika, M. Firdaus, S. Jang, and K. H. Rhee, Blockchain-enabled 5G edge networks and beyond: An intelligent cross-silo federated learning approach, Secur. Commun. Networks, vol. 2021, p. 5550153, 2021.
Z. B. Zheng, S. A. Xie, H. N. Dai, X. P. Chen, and H. Wang, Blockchain challenges and opportunities: A survey, Int. J. Web Grid Serv., vol. 14, no. 4, pp. 352–375, 2018.
R. Kumar, A. A. Khan, J. Kumar, , N. A. Golilarz, S. M. Zhang, Y. Ting, C. Y. Zheng, and W. Y. Wang, Blockchain-federated-learning and deep learning models for COVID-19 detection using CT imaging, IEEE Sens. J., vol. 21, no. 14, pp. 16301–16314, 2021.
H. N. Dai, Z. B. Zheng, and Y. Zhang, Blockchain for internet of things: A survey, IEEE Internet Things J., vol. 6, no. 5, pp. 8076–8094, 2019.
M. A. Ferrag and L. Shu, The performance evaluation of blockchain-based security and privacy systems for the internet of things: A tutorial, IEEE Internet Things J., vol. 8, no. 24, pp. 17236–17260, 2021.
K. Peng, M. J. Li, H. J. Huang, C. Wang, S. H. Wan, and K. K. R. Choo, Security challenges and opportunities for smart contracts in internet of things: A survey, IEEE Internet Things J., vol. 8, no. 15, pp. 12004–12020, 2021.
G. J. Joyia, R. M. Liaqat, A. Farooq, and S. Rehman, Internet of medical things (IoMT): Applications, benefits and future challenges in healthcare domain, J. Commun., vol. 12, no. 4, pp. 240–247, 2017.
K. D. Krishna, V. Akkala, R. Bharath, P. Rajalakshmi, and A. M. Mohammed, FPGA based preliminary CAD for kidney on IoT enabled portable ultrasound imaging system, in Proc. IEEE 16th Int. Conf. on e-Health Networking, Applications and Services (Healthcom), Natal, Brazil, 2015, pp. 257–261.
H. S. Zhao, J. P. Shi, X. J. Qi, X. G. Wang, and J. Y. Jia, Pyramid scene parsing network, in Proc. 2017 IEEE Conf. Computer Vision and Pattern Recognition, Honolulu, HI, USA, 2017, pp. 6230–6239.
S. Woo, J. Park, J. Y. Lee, and I. S. Kweon, CBAM: Convolutional block attention module, in Proc. 15th European Conf. Computer Vision, Munich, Germany, 2018, pp. 3–19.
K. M. He, X. Y. Zhang, S. Q. Ren, and J. Sun, Deep residual learning for image recognition, in Proc. 2016 IEEE Conf. Computer Vision and Pattern Recognition, Las Vegas, NV, USA, 2016, pp. 770–778.
B. Vidakovic, Discrete Wavelet Transformation. Hoboken, NJ, USA: John Wiley & Sons, 2008.
D. Mazières, Self-certifying file system, PhD dissertation, Massachusetts Institute of Technology, Cambridge, UK, 2000.
J. Benet, IPFS-content addressed, versioned, p2p file system, arXiv preprint arXiv:1407.3561, 2014.
J. A. Smith and O. C. Jensen, Portable ultrasound scanner, U.S. Patent US20110118562A1, 2011.
R. Bharath, D. Chandrashekar, V. Akkala, D. Krishna, H. Ponduri, P. Rajalakshmi, and U. B. Desai, Portable ultrasound scanner for remote diagnosis, in Proc. 17th Int. Conf. on E-Health Networking, Application & Services, Boston, MA, USA, 2016, pp. 211–216.
S. K. Fernbach, M. Maizels, and J. J. Conway, Ultrasound grading of hydronephrosis: Introduction to the system used by the Society for Fetal Urology, Pediatr. Radiol., vol. 23, no. 6, pp. 478–480, 1993.
A. Onen, An alternative grading system to refine the criteria for severity of hydronephrosis and optimal treatment guidelines in neonates with primary UPJ-type hydronephrosis, J. Pediatr. Urol., vol. 3, no. 3, pp. 200–205, 2007.
L. Xu and J. H. Xiang, ComboLoss for facial attractiveness analysis with squeeze-and-excitation networks, arXiv preprint arXiv:2010.10721, 2020.
M. A. Rahman and Y. Wang, Optimizing intersection-over-union in deep neural networks for image segmentation, in Proc. 12th Int. Symp. on Visual Computing, Las Vegas, NV, USA, 2016, pp. 234–244.
Tsinghua Science and Technology
Pages 1-12
Cite this article:
Guan Y, Wen P, Li J, et al. Deep Learning Blockchain Integration Framework for Ureteropelvic Junction Obstruction Diagnosis Using Ultrasound Images. Tsinghua Science and Technology, 2024, 29(1): 1-12.








Web of Science






Received: 14 March 2022
Revised: 12 April 2022
Accepted: 07 June 2022
Published: 21 August 2023
© The author(s) 2024.

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