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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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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).

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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. https://doi.org/10.26599/TST.2022.9010016

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

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/).

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