Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence

This study aimed to explore the application of pelvic floor ultrasound under particle swarm intelligent optimization algorithm in the preoperative and postoperative evaluation of female stress urinary incontinence (SUI) and provide a theoretical basis for clinical diagnosis. In this study, 90 patien...

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Autores principales:	Hongbin Zhang, Hezhou Li, Xin Zhao, Juan Wu, Xiao Liang, Haiyan Lu
Formato:	article
Lenguaje:	EN
Publicado:	Hindawi Limited 2021
Materias:	Computer software QA76.75-76.765
Acceso en línea:	https://doaj.org/article/9490adc3e20d43819e3e26caa8f37e86
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spelling	oai:doaj.org-article:9490adc3e20d43819e3e26caa8f37e862021-11-08T02:35:53ZPelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence1875-919X10.1155/2021/6517725https://doaj.org/article/9490adc3e20d43819e3e26caa8f37e862021-01-01T00:00:00Zhttp://dx.doi.org/10.1155/2021/6517725https://doaj.org/toc/1875-919XThis study aimed to explore the application of pelvic floor ultrasound under particle swarm intelligent optimization algorithm in the preoperative and postoperative evaluation of female stress urinary incontinence (SUI) and provide a theoretical basis for clinical diagnosis. In this study, 90 patients with SUI were enrolled, which were randomly and equally assigned into a blank group (healthy physical examination), control group (perineal ultrasound), and experimental group (pelvic floor ultrasound based on particle swarm intelligence optimization algorithm). The ultrasonic image segmentation and processing were carried out by a particle swarm intelligence optimization algorithm. Patients with stress incontinence were classified as group A, and patients without stress incontinence were classified as group B. The results of previous surgical examinations were the standard to judge the accuracy of pelvic floor ultrasound diagnosis based on the swarm intelligence optimization algorithm. The accuracy of diagnosing stress UI in the experimental group was 90.38%, which was significantly higher than that of the control group (54.31%) and the blank group (38.95%) (P < 0.05). The formation percentage of the urethral internal orifice in the experimental group was 82.5%, which was significantly higher than that of the control group (65.4%) and the blank group (12.5%), and there was a statistical difference among the groups (P < 0.05). In the resting state, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was greater than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was smaller than in the blank group, and there were statistical differences among the groups (P < 0.05). In the state of Valsalva, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was smaller than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was greater than that in group B. The distance of the bladder neck shifting downward was greater than that in group B, and there were statistical differences among the groups (P < 0.05). In short, the pelvic floor ultrasound based on the particle swarm intelligent optimization algorithm was accurate in the diagnosis of stress UI. The application of pelvic floor ultrasound in the diagnosis of UI provided image data objectively for clinical diagnosis and had a high application value.Hongbin ZhangHezhou LiXin ZhaoJuan WuXiao LiangHaiyan LuHindawi LimitedarticleComputer softwareQA76.75-76.765ENScientific Programming, Vol 2021 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Computer software QA76.75-76.765
spellingShingle	Computer software QA76.75-76.765 Hongbin Zhang Hezhou Li Xin Zhao Juan Wu Xiao Liang Haiyan Lu Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
description	This study aimed to explore the application of pelvic floor ultrasound under particle swarm intelligent optimization algorithm in the preoperative and postoperative evaluation of female stress urinary incontinence (SUI) and provide a theoretical basis for clinical diagnosis. In this study, 90 patients with SUI were enrolled, which were randomly and equally assigned into a blank group (healthy physical examination), control group (perineal ultrasound), and experimental group (pelvic floor ultrasound based on particle swarm intelligence optimization algorithm). The ultrasonic image segmentation and processing were carried out by a particle swarm intelligence optimization algorithm. Patients with stress incontinence were classified as group A, and patients without stress incontinence were classified as group B. The results of previous surgical examinations were the standard to judge the accuracy of pelvic floor ultrasound diagnosis based on the swarm intelligence optimization algorithm. The accuracy of diagnosing stress UI in the experimental group was 90.38%, which was significantly higher than that of the control group (54.31%) and the blank group (38.95%) (P < 0.05). The formation percentage of the urethral internal orifice in the experimental group was 82.5%, which was significantly higher than that of the control group (65.4%) and the blank group (12.5%), and there was a statistical difference among the groups (P < 0.05). In the resting state, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was greater than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was smaller than in the blank group, and there were statistical differences among the groups (P < 0.05). In the state of Valsalva, the vertical spacing y between the neck of the bladder and the edge of the pubis of patients in group B was smaller than that in group B, the horizontal spacing x between the neck of the bladder and the edge of the pubis was greater than that in group B. The distance of the bladder neck shifting downward was greater than that in group B, and there were statistical differences among the groups (P < 0.05). In short, the pelvic floor ultrasound based on the particle swarm intelligent optimization algorithm was accurate in the diagnosis of stress UI. The application of pelvic floor ultrasound in the diagnosis of UI provided image data objectively for clinical diagnosis and had a high application value.
format	article
author	Hongbin Zhang Hezhou Li Xin Zhao Juan Wu Xiao Liang Haiyan Lu
author_facet	Hongbin Zhang Hezhou Li Xin Zhao Juan Wu Xiao Liang Haiyan Lu
author_sort	Hongbin Zhang
title	Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
title_short	Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
title_full	Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
title_fullStr	Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
title_full_unstemmed	Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence
title_sort	pelvic floor ultrasound under particle swarm intelligent optimization algorithm in preoperative and postoperative evaluation of female stress urinary incontinence
publisher	Hindawi Limited
publishDate	2021
url	https://doaj.org/article/9490adc3e20d43819e3e26caa8f37e86
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Pelvic Floor Ultrasound under Particle Swarm Intelligent Optimization Algorithm in Preoperative and Postoperative Evaluation of Female Stress Urinary Incontinence

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