scholarly journals Deep Learning-Based Analysis of Efficiency and Surgical Timing for Patients with Cervical Insufficiency Using Transvaginal Ultrasound Images

2022 ◽  
Vol 2022 ◽  
pp. 1-8
Author(s):  
Xuekui Ye ◽  
Li Zhang ◽  
Rongxia Liu ◽  
Yongjuan Liu ◽  
Guowei Jiang
Keyword(s):  
Gestational Age ◽  
Transvaginal Ultrasound ◽  
Ultrasound Image ◽  
Control Group ◽  
Ultrasound Images ◽  
Generation Model ◽  
Cervical Insufficiency ◽  
Observation Group ◽  
Surgical Timing ◽  
Opening Width

Objective. This work aims to analyze the surgical timing and clinical efficacy of transvaginal cervical ring ligation based on the ultrasound image focus detection of patients with cervical insufficiency (CIC) under the ultrasound image theme generation model. Methods. 134 CIC patients who came to the hospital for ultrasound imaging diagnosis were collected. Observation group was treated with cervical cerclage (CC) and the pregnancy outcome was followed up. Control group was treated conservatively. Results. For patients in the control group, average gestational age was 21.12 ± 2.18 weeks, average cervical length (CL) was 15.54 ± 0.42 mm, and average uterine opening width was 3.06 ± 0.63 mm. In the observation group, average gestational age was 24.45 ± 4.12 weeks, average CL was 17.32 ± 4.09 mm, and average uterine opening width was 0.21 mm. There were significant differences between the two groups ( P < 0.05 ). There were also significant differences in the degree of uterine orifice dilation between the two groups ( P < 0.05 ). Pregnancy outcomes of the two groups were compared, and χ2 and P < 0.05 indicated significant differences. Conclusion. Convolution neural network (CNN) and long short-term memory model (LSTM) algorithm were used to classify patients' ultrasound images, which could effectively improve diagnosis and treatment efficiency. Surgical success rate, clinical outcomes, neonatal survival rate, and clinical effect of observation group were better than those of control group. Cervical ligation was best performed before 17 weeks of pregnancy in CIC.

scholarly journals Perinatal Mood Disorders in Second Trimester Pregnant Women with Cervical Insufficiency, a Population-based Cohort Study

2021 ◽  
Author(s):  
Chunqi Luo ◽  
Qiaojian Zou ◽  
Huiling Liang ◽  
Jingyi Chen ◽  
Xuanmin Chen ◽  
...  
Keyword(s):  
Pregnant Women ◽  
Mood Disorders ◽  
Protective Factor ◽  
Multiple Logistic Regression Analysis ◽  
Anxiety And Depression ◽  
Control Group ◽  
Cervical Insufficiency ◽  
Observation Group ◽  
Second Trimester ◽  
Self Rating

Abstract Background: Perinatal mood disorders can seriously endanger the health of pregnant women and fetus, affect family relationships and cause heavy burden and potential hazards to family and society. This study aims to investigate anxiety and depression in second trimester pregnant women with cervical insufficiency (CI) and identify its risk factors, so as to provide guidance for daily clinic work.Methods: From April 2019 to July 2020, 98 mid-pregnancy women with CI underwent laparoscopic cervical cerclage in the First Affiliated Hospital of Sun Yat-sen University were selected as observation group and 166 normal pregnant women in second trimester were set as control group. Zung's Self-Rating Anxiety Scale (SAS) and Self-Rating Depression Scale (SDS) were applied to evaluate perinatal mood disorders in both groups.Results: Pregnant women in CI group had a SAS score of 46.31±11.29 and SDS score of 54.12±11.72, higher than the SAS score of 41.63±7.70 and SDS score of 47.56±9.31 in control group (both P<0.001). While 32.65% and 67.35% of pregnant women in observation group were considered to have different degrees of anxiety and depression, only 15.06% and 30.72% of normal pregnant women meet the same condition (both P<0.001). Multiple logistic regression analysis indicated that educational experience is an independent protective factor for depression disorder in second trimester pregnant women with CI.Conclusion: Pregnant women with CI are prone to develop anxiety and depression in the second trimester than normal pregnant women, therefore doctors and nurses should pay more attention to them in clinic work.

scholarly journals Comparing Efficacy of Cerclage and Adjunctive Therapy (Cerclage & Pessary) in Prevention of Preterm Birth in Pregnant Women with Cervical Insufficiency: A Randomized Clinical Trial.

2020 ◽  
Author(s):  
Mitra Arjmandifar ◽  
Maryam Moshfeghi ◽  
Maryam Mohammadi ◽  
Mahya Eftekhari
Keyword(s):  
Clinical Trial ◽  
Survival Analysis ◽  
Preterm Birth ◽  
Pregnant Women ◽  
Randomized Clinical Trial ◽  
Gestational Age ◽  
Adjunctive Therapy ◽  
Intervention Group ◽  
Control Group ◽  
Cervical Insufficiency

Abstract Background: Cervical insufficiency is the responsible factor for 15-25% of pregnancy loss in the second trimester. Midwifery specialists sometimes prefer to use adjunctive therapy in combination with cerclage surgery for management of cervical insufficiency. The aim of this study was to evaluate the effectiveness of adjunctive pessary therapy after cerclage in improving perinatal and neonatal outcome and increasing satisfaction in women with cervical insufficiency.Methods: This concurrent randomized clinical trial was conducted at the infertility department of Royan Institute, Tehran, Iran from May 2018 to May 2020. In this trial, 170 singleton pregnant women, diagnosed with cervical insufficiency, of gestational age 14 to 26 weeks, were enrolled. Patients were randomized 1:1 to receive either cervical cerclage or pessary after cerclage. The primary outcomes were gestational age at the time of delivery and the percentage of preterm labor (<37 weeks). The secondary outcomes were the method of delivery, neonatal outcomes, maternal adverse events and maternal satisfaction of interventions.Results: Preterm birth before 37 weeks of gestation occurred in 16 women (19.3%) in the pessary group and 17 women (21%) in the control group (between-group difference, 1.11%; 95%CI 0.518−2.388%). In the survival analysis to 37 WK of gestation, the incidence of preterm birth was not significantly different between the two groups (Relative Risk (RR), 1; 95%CI, 0.161-6.202). Based on survival analysis, the incidence of vaginal bleeding and pelvic pain significantly differed between the two groups (RR, 2.68; 95%CI (1.31-5.46)) and (RR, 1.73; 95%CI (1.04-2.87), respectively. The mean score of satisfaction in the intervention group (5.73) was significantly higher than the control group (5.25), (between-group difference, 0.47; 95%CI (0.10-0.84).Conclusions: The placement of an adjunctive pessary for pregnant women with singleton pregnancy and a cervical insufficiency, did not result in a lower rate of preterm delivery before 37 weeks of gestation compared to cerclage alone. However, the complications of pregnancy after the intervention until delivery, were less in these women, while the level of satisfaction was higher. Trial registration: Iranian Registry of Clinical Trials (IRCT20180302038914N1), May 5,2018.

scholarly journals Deep Learning-Based Three-dimensional Transvaginal Ultrasound in Diagnosis of Intrauterine Adhesion

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ji Li ◽  
Dan Liu ◽  
Xiaofeng Qing ◽  
Lanlan Yu ◽  
Huizhen Xiang
Keyword(s):  
Deep Learning ◽  
Learning Algorithm ◽  
Transvaginal Ultrasound ◽  
Three Dimensional ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Diagnostic Efficiency ◽  
Research Subjects ◽  
Partial Differential ◽  
Intrauterine Adhesion

This study was aimed to enhance and detect the characteristics of three-dimensional transvaginal ultrasound images based on the partial differential algorithm and HSegNet algorithm under deep learning. Thereby, the effect of quantitative parameter values of optimized three-dimensional ultrasound image was analyzed on the diagnosis and evaluation of intrauterine adhesions. Specifically, 75 patients with suspected intrauterine adhesion in hospital who underwent the hysteroscopic diagnosis were selected as the research subjects. The three-dimensional transvaginal ultrasound image was enhanced and optimized by the partial differential equation algorithm and processed by the deep learning algorithm. Subsequently, three-dimensional transvaginal ultrasound examinations were performed on the study subjects that met the standards. The March classification method was used to classify the patients with intrauterine adhesion. Finally, the results by the three-dimensional transvaginal ultrasound were compared with the diagnosis results in hysteroscope surgery. The results showed that the HSegNet algorithm model realized the automatic labeling of intrauterine adhesion in the transvaginal ultrasound image and the final accuracy coefficient was 97.3%. It suggested that the three-dimensional transvaginal ultrasound diagnosis based on deep learning was efficient and accurate. The accuracy of the three-dimensional transvaginal ultrasound was 97.14%, the sensitivity was 96.6%, and the specificity was 72%. In conclusion, the three-dimensional transvaginal examination can effectively improve the diagnostic efficiency of intrauterine adhesion, providing theoretical support for the subsequent diagnosis and grading of intrauterine adhesion.

scholarly journals Deep-Learning-Based Color Doppler Ultrasound Image Feature in the Diagnosis of Elderly Patients with Chronic Heart Failure Complicated with Sarcopenia

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Peng Bian ◽  
Xiyu Zhang ◽  
Ruihong Liu ◽  
Huijie Li ◽  
Qingqing Zhang ◽  
...  
Keyword(s):  
Deep Learning ◽  
Doppler Ultrasound ◽  
Color Doppler ◽  
Ultrasound Image ◽  
Color Doppler Ultrasound ◽  
Control Group ◽  
Learning Technology ◽  
Ultrasound Images ◽  
Before And After ◽  
Experimental Group

The neural network algorithm of deep learning was applied to optimize and improve color Doppler ultrasound images, which was used for the research on elderly patients with chronic heart failure (CHF) complicated with sarcopenia, so as to analyze the effect of the deep-learning-based color Doppler ultrasound image on the diagnosis of CHF. 259 patients were selected randomly in this study, who were admitted to hospital from October 2017 to March 2020 and were diagnosed with sarcopenia. Then, all of them underwent cardiac ultrasound examination and were divided into two groups according to whether deep learning technology was used for image processing or not. A group of routine unprocessed images was set as the control group, and the images processed by deep learning were set as the experimental group. The results of color Doppler images before and after processing were analyzed and compared; that is, the processed images of the experimental group were clearer and had higher resolution than the unprocessed images of the control group, with the peak signal-to-noise ratio (PSNR) = 20 and structural similarity index measure (SSIM) = 0.09; the similarity between the final diagnosis results and the examination results of the experimental group (93.5%) was higher than that of the control group (87.0%), and the comparison was statistically significant ( P < 0.05 ); among all the patients diagnosed with sarcopenia, 88.9% were also eventually diagnosed with CHF and only a small part of them were diagnosed with other diseases, with statistical significance ( P < 0.05 ). In conclusion, deep learning technology had certain application value in processing color Doppler ultrasound images. Although there was no obvious difference between the color Doppler ultrasound images before and after processing, they could all make a better diagnosis. Moreover, the research results showed the correlation between CHF and sarcopenia.

scholarly journals Risk Factors of Tumor Patients with Peripherally Inserted Central Catheter Related Thrombosis and Nursing Intervention Analysis Mediated by Algorithm-Based Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Lifei Chen ◽  
Yingying Hu
Keyword(s):  
Risk Factors ◽  
Image Quality ◽  
Nursing Intervention ◽  
Control Group ◽  
Ultrasound Images ◽  
Axillary Vein ◽  
Observation Group ◽  
Peripherally Inserted Central Catheter ◽  
Central Catheter ◽  
Tumor Patients

This paper aimed to explore the risk factors of peripherally inserted central catheter (PICC) related thrombosis (PICC-RT) in tumor patients mediated by ultrasound images under minimum variance (MV) algorithm and put forward the corresponding nursing intervention methods based on the risk factors. The smoothing algorithm, diagonal loading algorithm, and coherence factor algorithm were optimized via MV algorithm. The optimized algorithm was compared with other algorithms to analyze its image quality and image processing speed. Literature retrieval was conducted to analyze the risk factors of PICC-RT in tumor patients. Tumor patients who received PICC for chemotherapy in the hospital from June 2018 to December 2019 were selected. Patients who were accepted before the experiment were taken as the controls, and there were control group (89 cases) and observation group (91 cases). Exercise, average flow rate of axillary vein per unit time, and PICC-RT were compared between the two groups. The results showed that the optimized algorithm (MV-N) had better image resolution, image contrast, and calculation speed than other algorithms. Gender, body mass index (BMI), pathological type, clinical stage, disease history, fibrinogen (FIB), and use of anticoagulant drugs were risk factors for PICC-RT in tumor patients. The number of PICC-RT and complications in the observation group was notably lower in contrast to the control group ( P < 0.05 ). It indicated that a novel algorithm was successfully established, which could increase the ultrasonic image quality and computing speed, and upper limb exercise could reduce the incidence of PICC-RT and its complications in tumor patients.

scholarly journals Pattern Classification-Based Analysis on Gastrointestinal Multifunctional Apparatus Guided by Ultrasound Image after Resection of Esophageal Carcinoma

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Ping Chen ◽  
Wenmin Lu ◽  
Jue Wang ◽  
Zhanling Guo ◽  
Lianchang Liu
Keyword(s):  
Esophageal Cancer ◽  
Cancer Patients ◽  
Pattern Classification ◽  
Gastrointestinal Motility ◽  
Ultrasound Image ◽  
Abdominal Distension ◽  
Control Group ◽  
Observation Group ◽  
Image Guided ◽  
The Difference

To investigate the diagnostic value of ultrasound image-guided extracorporeal gastrointestinal multifunctional instrument based on pattern classification algorithm (PCA) on the physical status of patients after esophagectomy for esophageal cancer (EC), in this study, 120 esophageal cancer patients who entered our hospital for consultation and treatment from July 2019 to July 2020 were selected as the investigation subjects, and the patients were randomly divided into a control group (gastrointestinal motility drug treatment) and an observation group (gastrointestinal motility drug + extracorporeal gastrointestinal multifunctional apparatus (EGMA) therapy), with 60 cases in each group. A pattern classification method algorithm was designed for the ultrasound image characteristics of esophageal cancer tumors and applied to the clinical identification and diagnosis of postoperative status of esophageal cancer patients by the ultrasound image-guided extracorporeal gastrointestinal multifunctional instrument. The results showed that the time of exhaustion, the time of recovery of bowel sounds, and the time of the beginning of gastrointestinal peristalsis in the observation group were better than those in the control group, and the difference between the two groups was statistically significant ( P < 0.05 ); the incidence of postoperative abdominal distension in the observation group was 15%, and that in the control group was 28.3%; the incidence of postoperative abdominal distension in the observation group was significantly lower than that in the control group, and the difference was statistically significant ( P < 0.05 ). In conclusion, the use of EGMA guided by ultrasound image based on PCA can effectively improve the gastrointestinal function of esophageal cancer patients and significantly reduce the incidence of postoperative complications, which is worthy of clinical promotion.

scholarly journals Back Propagation Neural Network-Based Ultrasound Image for Diagnosis of Cartilage Lesions in Knee Osteoarthritis

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Xiaoming Zhao ◽  
Wei Gong ◽  
Xing Li ◽  
Weibing Yang ◽  
Dengfeng Yang ◽  
...  
Keyword(s):  
Neural Network ◽  
Knee Osteoarthritis ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Ultrasound Image ◽  
Kappa Coefficient ◽  
Control Group ◽  
Ultrasound Images ◽  
The Difference ◽  
Experimental Group

Objective. To explore the application value of ultrasound image based on back propagation (BP) neural network algorithm in knee osteoarthritis (KOA) and evaluate the application effect and value of ultrasound image technology based on the BP neural network in the diagnosis of knee osteoarthritis cartilage lesions, 98 patients who were admitted to our hospital were diagnosed with KOA and had undergone arthroscopic soft tissue examinations were randomly selected. According to whether image processing was performed, the ultrasound images of all patients were divided into two groups. The control group was image before processing, and the experimental group was image after processing optimization. The consistency of the inspection results of the ultrasound images before and after the processing with the arthroscopy results was compared. The results showed that the staging accuracy of the control group was 68.3% and that of the experimental group was 76.9%. The accuracy of staging cartilage degeneration of the experimental group was higher than that of the control group, and the difference was not remarkable ( P > 0.05 ). The kappa coefficient of the experimental group was 0.61, and that of the control group was 0.40. The kappa coefficient of the experimental group was higher than that of the control group, and the difference was significant ( P < 0.05 ). Conclusion. The inspection effect of the ultrasound image processed by the BP neural network was superior to that of the conventional ultrasound image. It reflected the good adoption prospect of neural networks in image processing.

scholarly journals Effect of Interventional Therapy on Iliac Venous Compression Syndrome Evaluated and Diagnosed by Artificial Intelligence Algorithm-Based Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Ye Bai ◽  
Fei Bo ◽  
Wencan Ma ◽  
Hongwei Xu ◽  
Dawei Liu
Keyword(s):  
Artificial Intelligence ◽  
Ultrasound Image ◽  
Image Feature ◽  
Control Group ◽  
Ultrasound Images ◽  
Compression Syndrome ◽  
Venous Compression ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
And Control

In order to explore the efficacy of using artificial intelligence (AI) algorithm-based ultrasound images to diagnose iliac vein compression syndrome (IVCS) and assist clinicians in the diagnosis of diseases, the characteristics of vein imaging in patients with IVCS were summarized. After ultrasound image acquisition, the image data were preprocessed to construct a deep learning model to realize the position detection of venous compression and the recognition of benign and malignant lesions. In addition, a dataset was built for model evaluation. The data came from patients with thrombotic chronic venous disease (CVD) and deep vein thrombosis (DVT) in hospital. The image feature group of IVCS extracted by cavity convolution was the artificial intelligence algorithm imaging group, and the ultrasound images were directly taken as the control group without processing. Digital subtraction angiography (DSA) was performed to check the patient’s veins one week in advance. Then, the patients were rolled into the AI algorithm imaging group and control group, and the correlation between May–Thurner syndrome (MTS) and AI algorithm imaging was analyzed based on DSA and ultrasound results. Satisfaction of intestinal venous stenosis (or occlusion) or formation of collateral circulation was used as a diagnostic index for MTS. Ultrasound showed that the AI algorithm imaging group had a higher percentage of good treatment effects than that of the control group. The call-up rate of the DMRF-convolutional neural network (CNN), precision, and accuracy were all superior to those of the control group. In addition, the degree of venous swelling of patients in the artificial intelligence algorithm imaging group was weak, the degree of pain relief was high after treatment, and the difference between the artificial intelligence algorithm imaging group and control group was statistically considerable ( p < 0.005 ). Through grouped experiments, it was found that the construction of the AI imaging model was effective for the detection and recognition of lower extremity vein lesions in ultrasound images. To sum up, the ultrasound image evaluation and analysis using AI algorithm during MTS treatment was accurate and efficient, which laid a good foundation for future research, diagnosis, and treatment.

scholarly journals Cluster-Based Immunotherapy for Patients with Recurrent Abortion Caused by Antiphospholipid Syndrome

2021 ◽  
Vol 2021 ◽  
pp. 1-5
Author(s):  
Ruifang Wang ◽  
Juanjuan Yu ◽  
Zhen Yan ◽  
Xiaolin Cheng ◽  
Jian Chen ◽  
...  
Keyword(s):  
Antiphospholipid Syndrome ◽  
Clustering Algorithm ◽  
Clinical Symptoms ◽  
Recurrent Miscarriage ◽  
Control Group ◽  
Antiphospholipid Antibody ◽  
Antiphospholipid Antibody Syndrome ◽  
Ultrasound Images ◽  
Observation Group ◽  
Hormone Levels

In order to diagnose patients with pregnancy and antiphospholipid antibody syndrome, provide early treatment, and effectively reduce the pregnancy outcome of the abnormal pregnancy, the effect of antiphospholipid syndrome (APS) immunotherapy on the incidence of abortion was discussed based on clustering algorithm. We selected 62 cases of APS leading to recurrent miscarriage patients, in the early pregnancy injection of low molecular heparin, intravenous drip proprocyclin, and oral tactics, using B-ultrasound images to observe the pregnancy ending. The results show that the hormone levels in the two groups were different before treatment ( P > 0.05 ); after treatment, the HCG, E2, and P hormone levels in the two groups were significantly improved, and the HCG, E2, and P hormone levels in the observation group were significantly higher than those of the control group ( P < 0.05 ); the abortion rate of patients in the observation group was significantly lower than that of the control group ( P < 0.05 ); the antiphosphorus antibody of the study group was significantly higher than that of the control group. For APS patients, immunotherapy is effective. Antiphospholipid syndrome causes remarkable immunotherapy effect in patients with recurrent miscarriage, effectively improves the clinical symptoms of patients, improves antiprochemical antibody rosion, and improves the patient’s pregnancy outcomes, which is worth promoting.

scholarly journals Support Vector Machine-Based Analysis of Holmium Laser Lithotripsy in Treatment of Urinary Calculus from Ultrasound Images

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Hu Li ◽  
Zhijun Chen ◽  
Dezhi Kong ◽  
Zhiqiang Huang ◽  
Ningning Wang
Keyword(s):  
Support Vector Machine ◽  
Success Rate ◽  
Laser Lithotripsy ◽  
Holmium Laser ◽  
Support Vector ◽  
Control Group ◽  
Ultrasound Images ◽  
Observation Group ◽  
Holmium Laser Lithotripsy ◽  
Svm Algorithm

This study was to investigate the application value of ultrasound images optimized by support vector machine (SVM) algorithm in the efficacy analysis of holmium laser lithotripsy in the treatment of urinary calculi. 92 patients for treatment of UC were selected as research subjects, with 46 cases in each group. The control group received pneumatic lithotripsy for diagnosis and treatment. The observation group received holmium laser lithotripsy for calculus treatment. The perimeter and area of the defect and the length and width of the external distance of the most effective area of the defect were used as classification features, and a classifier based on SVM was constructed and applied to it. After treatment, the success rate, operation duration, stone clearance time, and hospital stay of the two groups were comprehensively evaluated. The results showed that the success rate of the observation group adopting holmium laser lithotripsy was 100%. The duration of operation in the observation group was (29.7 ± 7.65) min, and the time to clear calculus was (6.99 ± 5.29) days. The length of hospital stay was (3.67 ± 2.9) days. The probability of complications in the observation group was 3.11%. The observation group was superior to the control group in all surgical indicators (95%, 40.7 ± 8.36 minutes, 14.1 ± 7.21 days, and 5.12 ± 3.72 days), with considerable differences between groups ( P < 0.05 ). The strong support structure similarity information improved the detection and diagnostic analysis ability of ultrasonic images. In conclusion, after ultrasound image evaluation based on SVM algorithm, the adoption of holmium laser lithotripsy can effectively improve the success rate of patients with urinary system disease, which was worthy of clinical adoption and promotion.

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