Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning

doi:10.1007/s11227-021-04002-0

科研成果详情

题名	Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning
作者	Sun, Xiaoying 1,2; Lu, Qiaoli 3
发表日期	2022-02-01
发表期刊	Journal of Supercomputing 影响因子和分区
语种	英语
原始文献类型	Journal article (JA)
关键词	Blood Blood vessels Diagnosis Diseases Image reconstruction Learning algorithms Patient treatment Pulse shaping circuits Ultrasonics 3-D reconstruction algorithms Contrast enhanced ultrasound Conventional treatments Diabetic nephropathy Hemodynamic parameters Peripheral blood vessels Three-dimensional (3-D) reconstruction Ultrasound examination
摘要	Abstract: To evaluate the efficacy of atorvastatin in the treatment of diabetic nephropathy, contrast-enhanced ultrasound (CEUS) based on Poisson three-dimensional (3D) reconstruction algorithm was used. Poisson 3D reconstruction algorithm was optimized based on Gaussian filtering deep learning. Then, the running time, occupied space, and number of triangular blocks of Poisson 3D reconstruction algorithm before and after optimization were analyzed and compared through simulation experiments. One hundred and fifty-six DN patients were divided into an experimental group (conventional treatment + atorvastatin) and a control group (conventional treatment). Ultrasound examinations were performed on all patients before and after treatment, and the artificial intelligence (AI) "Doctor You" system was adopted for image transmission and diagnosis. The kidney volume (V) and hemodynamic parameters, including the maximal kidney volume (Vmax), minimal kidney volume (Vmin), and resistance index of all patients were detected and recorded before and after the treatment. The end-point events of patients were tracked. The results showed that the running time of the optimized Poisson 3D reconstruction algorithm was notably shorter, and it occupied more space compared with the pre-optimized Poisson 3D reconstruction algorithm, and the difference was remarkable (P 3) in experimental group after the treatment was smaller in contrast to the control group (159.11 ± 31.79 cm3) (P © 2021, The Author(s), under exclusive licence to Springer Science+Business Media, LLC, part of Springer Nature.
出版者	Springer
ISSN	0920-8542
EISSN	1573-0484
卷号	78 期号:3 页码:3539-3560
DOI	10.1007/s11227-021-04002-0
收录类别	EI
EI入藏号	20213110706933
EI主题词	Deep learning
EI分类号	461.2 Biological Materials and Tissue Engineering ; 461.6 Medicine and Pharmacology ; 713.4 Pulse Circuits ; 753.1 Ultrasonic Waves
引用统计	被引频次[WOS]：0 [WOS记录] [WOS相关记录]
文献类型	期刊论文
条目标识符	https://kms.wmu.edu.cn/handle/3ETUA0LF/153468
专题	温州医科大学
通讯作者	Lu, Qiaoli
作者单位	1.School of Nursing, Medical College, Soochow University, Jiangsu, Suzhou; 215006, China; 2.Department of Emergency, The Qinghai Provincial People’s Hospital, Qinghai, Xining; 810007, China; 3.Department of General Practice, Zhuji People’s Hospital of Zhejiang Province, Zhejiang Province, Shaoxing; 311800, China
推荐引用方式 GB/T 7714	Sun, Xiaoying,Lu, Qiaoli. Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning[J]. Journal of Supercomputing,2022,78(3):3539-3560.
APA	Sun, Xiaoying, & Lu, Qiaoli. (2022). Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning. Journal of Supercomputing, 78(3), 3539-3560.
MLA	Sun, Xiaoying,et al."Contrast-enhanced ultrasound in optimization of treatment plans for diabetic nephropathy patients based on deep learning".Journal of Supercomputing 78.3(2022):3539-3560.