Journal Home > Volume 29 , Issue 1
Tsinghua Science and Technology 2024, 29(1): 1-12 https://doi.org/10.26599/TST.2022.9010016
Open Access | Issue | Published: 21 August 2023

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

Show Author's Information Yu Guan1,  Pengceng Wen1,  Jianqiang Li1( ),  Jinli Zhang1,  Xianghui Xie2
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Beijing Children’s Hospital Affiliated to Capital Medical University, Beijing 100020, China
Keywords:
machine learning, data mining, image processing and computer vision, medical information systems
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. https://doi.org/10.26599/TST.2022.9010016
Download citation
EndNote(RIS)
BibTeX

255

Views

33

Downloads

Citations

1

Crossref

1

WoS

1

Scopus

0

CSCD

Abstract Full text About this article

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).


menu
Abstract
Full text
Outline
About this article

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

Show Author's information Yu Guan1, Pengceng Wen1, Jianqiang Li1( ), Jinli Zhang1, Xianghui Xie2
Faculty of Information Technology, Beijing University of Technology, Beijing 100124, China
Beijing Children’s Hospital Affiliated to Capital Medical University, Beijing 100020, China

Abstract

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).

Keywords: machine learning, data mining, image processing and computer vision, medical information systems

References(40)

[1]
M. J. Siegel, Pediatric Sonography, 4th ed. Philadelphia, PA, USA: Lippincott Williams & Wilkins, 2011.
[2]
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.
DOI Google Scholar
[3]
B. A. Warady and V. Chadha, Chronic kidney disease in children: The global perspective, Pediatr. Nephrol., vol. 22, no. 12, pp. 1999–2009, 2007.
DOI Google Scholar
[4]
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.
DOI Google Scholar
[5]
H. Hashim and C. R. J. Woodhouse, Ureteropelvic junction obstruction, Eur. Urol. Suppl., vol. 11, no. 2, pp. 25–32, 2012.
DOI Google Scholar
[6]
A. K. Ucar and S. Kurugoglu, Urinary ultrasound and other imaging for ureteropelvic junction type hydronephrosis (UPJHN), Front. Pediatr., vol. 8, p. 546, 2020.
DOI Google Scholar
[7]
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.
DOI Google Scholar
[8]
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.
DOI Google Scholar
[9]
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.
DOI Google Scholar
[10]
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.
DOI Google Scholar
[11]
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.
DOI Google Scholar
[12]
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.
DOI Google Scholar
[13]
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.
DOI Google Scholar
[14]
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.
DOI Google Scholar
[15]
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.
DOI Google Scholar
[16]
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.
DOI Google Scholar
[17]
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.
DOI Google Scholar
[18]
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, .
DOI Google Scholar
[19]
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.
DOI Google Scholar
[20]
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.
DOI Google Scholar
[21]
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.
DOI Google Scholar
[22]
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.
DOI Google Scholar
[23]
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.
DOI Google Scholar
[24]
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.
DOI Google Scholar
[25]
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.
DOI Google Scholar
[26]
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.
DOI Google Scholar
[27]
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.
DOI Google Scholar
[28]
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.
DOI Google Scholar
[29]
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.
DOI Google Scholar
[30]
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.
DOI Google Scholar
[31]
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.
Google Scholar
[32]
B. Vidakovic, Discrete Wavelet Transformation. Hoboken, NJ, USA: John Wiley & Sons, 2008.
[33]
D. Mazières, Self-certifying file system, PhD dissertation, Massachusetts Institute of Technology, Cambridge, UK, 2000.
[34]
J. Benet, IPFS-content addressed, versioned, p2p file system, arXiv preprint arXiv:1407.3561, 2014.
Google Scholar
[35]
J. A. Smith and O. C. Jensen, Portable ultrasound scanner, U.S. Patent US20110118562A1, 2011.
[36]
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.
DOI Google Scholar
[37]
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.
DOI Google Scholar
[38]
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.
DOI Google Scholar
[39]
L. Xu and J. H. Xiang, ComboLoss for facial attractiveness analysis with squeeze-and-excitation networks, arXiv preprint arXiv:2010.10721, 2020.
Google Scholar
[40]
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.
DOI Google Scholar
Publication history
Copyright
Acknowledgements
Rights and permissions

Publication history

Received: 14 March 2022
Revised: 12 April 2022
Accepted: 07 June 2022
Published: 21 August 2023
Issue date: February 2024

Copyright

© The author(s) 2024.

Acknowledgements

This study was supported by the National Key R&D Program of China (No. 2020YFB2104402).

Rights and permissions

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).

Return