Deep Learning Blockchain Integration Framework for Ureteropelvic Junction Obstruction Diagnosis Using Ultrasound Images
作者机构：Faculty of Information TechnologyBeijing University of TechnologyBeijing 100124China Beijing Children’s Hospital Affiliated to Capital Medical UniversityBeijing 100020China
出 版 物：《Tsinghua Science and Technology》 (清华大学学报（自然科学版（英文版）)
年 卷 期：2024年第29卷第1期
页 面：1-12页
核心收录：
学科分类：08[工学] 1010[医学-医学技术（可授医学、理学学位）] 0831[工学-生物医学工程（可授工学、理学、医学学位）] 0710[理学-生物学] 1002[医学-临床医学] 081104[工学-模式识别与智能系统] 0805[工学-材料科学与工程（可授工学、理学学位）] 0811[工学-控制科学与工程] 081201[工学-计算机系统结构] 0701[理学-数学] 0812[工学-计算机科学与技术（可授工学、理学学位）] 0702[理学-物理学] 1009[医学-特种医学]
基 金：This study was supported by the National Key R&D Program of China(No.2020YFB2104402).
主 题：data mining image processing and computer vision machine learning medical information systems
摘 要：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).
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