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

【Author】 Guan, Yu; Wen, Pengceng; Li, Jianqiang; Zhang, Jinli; Xie, Xianghui

【Source】TSINGHUA SCIENCE AND TECHNOLOGY

【影响因子】3.515

【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】Deep learning; Federated learning; Ultrasonography; Benchmark testing; Blockchains; InterPlanetary File System; Task analysis; data mining; image processing and computer vision; machine learning; medical information systems

【发表时间】2024 FEB

【收录时间】2023-10-24

【文献类型】实验仿真

【主题类别】

区块链应用-实体经济-医疗领域