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

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Tsinghua Science and Technology 2024年第1期29卷 1-12页

作者： Yu Guan Pengceng Wen Jianqiang Li Jinli Zhang Xianghui Xie Faculty of information Technology Beijing University of TechnologyBeijing 100124China Beijing Children’s Hospital Affiliated to Capital medical University Beijing 100020China

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 MloU=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(loMT).

关键词： data mining image processing and computer vision machine learning medical information systems

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个人健康档案中基于指纹特征的个性化加密密钥(英文)

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China Communications 2011年第7期8卷 128-135页

作者：马国强刘娟倪斌 School of Computer Wuhan University University of South Carolina Columbia SC USA

An embedded method which can provide privacy-safeguard and data-security layer for the Personal Health Records (PHR) is proposed. In our method, the fingerprint image of a patient or doctor is obtained with fingerprint scanner and the values of fingerprint features points are calculated and saved in an IC card. As a result, saving the fingerprint image is not required in our method. Based on the user's password, a transformation is applied on the fingerprint topology structural values. After that, we take these points' coordinates on the transformed topology structure as a cryptographic key, which is used with Advanced Encryption Standard (AES) algorithm to encrypt the users' privacy information, such as prescription, laboratory sheet, medical certificate, etc. The experimental results demonstrate that our method could bring patients the self-control and self-management on their own medical privacy information.

关键词： system analysis and design health care medical information systems fingerprint identification

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