CNN-based diagnosis model of children's bladder compliance using a single intravesical pressure signal

被引：1

作者：

Yuan, Gang ^{[1
,2
]}

Ge, Zicong ^{[1
,2
]}

Zheng, Jian ^{[1
,2
]}

Yan, Xiangming ^{[3
]}

Fu, Mingcui ^{[3
]}

Li, Ming ^{[1
,2
]}

Yang, Xiaodong ^{[1
,2
]}

Tang, Liangfeng ^{[4
]}

机构：

[1] Univ Sci & Technol China, Sch Biomed Engn Suzhou, Div Life Sci & Med, Hefei, Peoples R China

[2] Chinese Acad Sci, Suzhou Inst Biomed Engn & Technol, Dept Med Imaging, Suzhou, Peoples R China

[3] Soochow Univ, Childrens Hosp, Dept Surg, Suzhou, Peoples R China

[4] Fudan Univ, Childrens Hosp, Dept Pediat Urol, Shanghai, Peoples R China

来源：

COMPUTER METHODS IN BIOMECHANICS AND BIOMEDICAL ENGINEERING | 2024年

关键词：

Bladder compliance; intravesical pressure; urodynamic study; convolutional neural network; LOWER URINARY-TRACT; CLASSIFICATION; ALGORITHM;

D O I：

10.1080/10255842.2023.2301414

中图分类号：

TP39 [计算机的应用];

学科分类号：

081203 ; 0835 ;

摘要：

Bladder compliance assessment is crucial for diagnosing bladder functional disorders, with urodynamic study (UDS) being the principal evaluation method. However, the application of UDS is intricate and time-consuming in children. So it'S necessary to develop an efficient bladder compliance screen approach before UDS. In this study, We constructed a dataset based on UDS and designed a 1D-CNN model to optimize and train the network. Then applied the trained model to a dataset obtained solely through a proposed perfusion experiment. Our model outperformed other algorithms. The results demonstrate the potential of our model to alert abnormal bladder compliance accurately and efficiently.

引用

页码：698 / 709

页数：12

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