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
收藏
页码：698 / 709
页数：12
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