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 Comparison of Short-Term Efficacy of Total Knee Arthroplasty Versus Arthroscopy in Chinese Patients with Knee Osteoarthritis

2020 ◽  
Author(s):  
Xiaolin Jia ◽  
San Cai ◽  
Wei Hu ◽  
Qiang Gan ◽  
Mingquan Zhou
Keyword(s):  
Total Knee Arthroplasty ◽  
Knee Osteoarthritis ◽  
Knee Arthroplasty ◽  
Scientific Basis ◽  
Knee Function ◽  
Chinese Patients ◽  
Control Group ◽  
Total Knee ◽  
The Difference ◽  
Experimental Group

Abstract Background: The purpose of this study was to compare the improvement of knee function in patients with knee osteoarthritis who underwent total knee arthroplasty and arthroscopy in China, and to provide a scientific basis for the application of clinical total knee arthroplasty in knee osteoarthritis.Methods: A total of 160 patients with knee osteoarthritis who were admitted to Chinese hospital from January 2017 to December 2018 were studied. They were divided into experimental group and control group according to their willingness of treatment. The control group was treated with arthroscopy and the experimental group was treated with total knee arthroplasty. All patients were followed for a period of 6 months. The knee joint function score (HSS), visual analog scale (VAS), and anxiety self-assessment scale (SAS) scores before and after surgery were compared between the two groups. Results: The proportion of "excellent or good" in the efficacy of the experimental group (91.25%) was higher than that of the control group (72.50%), and the difference was statistically significant (χ2=9.476, P<0.05). The HSS score of the experimental group was higher than that of the control group (P<0.05), while the VAS and SAS scores were lower than those of the control group (P<0.05). The scores of various SF-36 scales in the experimental group were higher than those in the control group after operation (P<0.05).Conclusions: Total knee arthroplasty was considered effective in treating patients with knee osteoarthritis that meet the indications, and is beneficial to improve knee function and reduce pain in patients. The surgical treatment also reduced the level of anxiety and effectively improve the quality of life of patients. Further investigation of its clinical application on treatment of knee osteoarthritis is warranted.

scholarly journals Prediction and Value of Ultrasound Image in Diagnosis of Fetal Central Nervous System Malformation under Deep Learning Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Yuehong Zhou
Keyword(s):  
Neural Network ◽  
Central Nervous System ◽  
Nervous System ◽  
Deep Learning ◽  
Ultrasound Examination ◽  
Ultrasound Image ◽  
Control Group ◽  
Test Results ◽  
Experimental Group ◽  
Central Nervous System Malformation

This study was to explore the application of deep learning neural network (DLNN) algorithms to identify and optimize the ultrasound image so as to analyze the effect and value in diagnosis of fetal central nervous system malformation (CNSM). 63 pregnant women who were gated in the hospital were suspected of being fetal CNSM and were selected as the research objects. The ultrasound images were reserved in duplicate, and one group was defined as the control group without any processing, and images in the experimental group were processed with the convolutional neural network (CNN) algorithm to identify and optimize. The ultrasound examination results and the pathological test results before, during, and after the pregnancy were observed and compared. The results showed that the test results in the experimental group were closer to the postpartum ultrasound and the results of the pathological result, but the results in both groups showed no statistical difference in contrast to the postpartum results in terms of similarity ( P > 0.05 ). In the same pregnancy stage, the ultrasound examination results of the experimental group were higher than those in the control group, and the contrast was statistically significant ( P < 0.05 ); in the different pregnancy stages, the ultrasound examination results in the second trimester were more close to the postpartum examination results, showing statistically obvious difference ( P < 0.05 ). In conclusion, ultrasonic image based on deep learning was higher in CNSM inspection; and ultrasonic technology had to be improved for the examination in different pregnancy stages, and the accuracy of the examination results is improved. However, the amount of data in this study was too small, so the representative was not high enough, which would be improved.

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.

Discriminative Neural Network for Coronary Heart Disease Detection

2020 ◽  
Vol 10 (2) ◽  
pp. 463-468
Author(s):  
Xiaoqing Gu ◽  
Yizhang Jiang ◽  
Tongguang Ni
Keyword(s):  
Neural Network ◽  
Coronary Heart Disease ◽  
Heart Disease ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Feature Space ◽  
Major Type ◽  
Good Decision ◽  
The Difference ◽  
Sensitivity Specificity

Cardiovascular disease is one of the commonest diseases and main causes of death over the world. As the major type of cardiovascular diseases, correct and timely diagnosis of coronary heart disease (CHD) is very essential. Traditional back-propagation (BP) neural network aims to train a multilayer feedforward neural network which transforms data into the feature space to learn good decision boundaries. However, the performance of BP neural network tends to deteriorate when dealing with complexity medical diagnostic tasks. To improve the detection of CHD, this study proposes a discriminative neural network called DNN. DNN explores discriminative information by maximizing the difference of the compactness within each class and separability between different classes. DNN integrates the discriminative information into the framework of BP neural network, and can be easily implemented by the existing neural network software. Experimental results on Z-Alizadeh Sani dataset show that DNN achieves satisfactory performance in sensitivity, specificity, accuracy and receiver operating characteristic (ROC) curve.

scholarly journals A Preliminary Study on the Safety, Efficacy and Acceptability of the Community Preparation of Siling Labuyo (Capsicum frutescens) Liniment in the Management of Knee Osteoarthritis in a Six-Week, Active-Controlled Community-Based Clinical Trial

2019 ◽  
Vol 53 (4) ◽  
Author(s):  
Elizabeth R. Paterno ◽  
Clarisse A. Pangilinan ◽  
Erna C. Arollado ◽  
Rachael Marie B. Rosario
Keyword(s):  
Clinical Trial ◽  
Knee Osteoarthritis ◽  
Control Agent ◽  
Control Group ◽  
Controlled Clinical Trial ◽  
Control Groups ◽  
Double Blind ◽  
The Difference ◽  
And Control ◽  
Experimental Group

Objective. The study determined the safety, efficacy and acceptability of a Philippine community preparation of Siling Labuyo liniment in the management of knee osteoarthritis. Methods. A 6-week randomized, double-blind, active-controlled clinical trial was conducted in three municipalities of Cavite from 2017-2018. The municipalities were randomly assigned to either the control or experimental group, using a commercially available Diclofenac 1% gel as the control agent. Knee Injury and Osteoarthritis Outcome Score (KOOS) and Pain Visual Analogue Scale (VAS) were used to measure the outcomes. Results. Forty-seven participants completed the study. Statistically significant improvement (p<0.05) in pain relief, reduction of symptoms and increase in knee functionality was reported by participants in both the experimental and control groups. Across the dimensions measured, at least 30% improvement in scores was reported by the experimental group, and at least 40% by the control group. The difference was statistically not significant (p>0.05). Itching (13%), burning sensation (11%) and reddening of the skin (15%) were experienced in both the experimental and the active control groups. Conclusion. Use of the liniment led to a modest therapeutic effect and was well-tolerated by the participants.

Economic benefit of shale gas exploitation based on back propagation neural network

2020 ◽  
Vol 39 (6) ◽  
pp. 8823-8830
Author(s):  
Jiafeng Li ◽  
Hui Hu ◽  
Xiang Li ◽  
Qian Jin ◽  
Tianhao Huang
Keyword(s):  
Neural Network ◽  
Shale Gas ◽  
Bp Neural Network ◽  
Large Scale ◽  
Linear Prediction ◽  
Back Propagation ◽  
Back Propagation Neural Network ◽  
Economic Benefits ◽  
Gas Well ◽  
Well Production

Under the influence of COVID-19, the economic benefits of shale gas development are greatly affected. With the large-scale development and utilization of shale gas in China, it is increasingly important to assess the economic impact of shale gas development. Therefore, this paper proposes a method for predicting the production of shale gas reservoirs, and uses back propagation (BP) neural network to nonlinearly fit reservoir reconstruction data to obtain shale gas well production forecasting models. Experiments show that compared with the traditional BP neural network, the proposed method can effectively improve the accuracy and stability of the prediction. There is a nonlinear correlation between reservoir reconstruction data and gas well production, which does not apply to traditional linear prediction methods

scholarly journals QOLP-17. GROUP MEDICAL PLAY EFFECTIVELY IMPROVES HOSPITALIZATIONADAPTABILITY OF PEDIATRIC PATIENTS WITH NEUROTUMOR

2020 ◽  
Vol 22 (Supplement_2) ◽  
pp. ii178-ii178
Author(s):  
Xing Zhang ◽  
Fuqiang Zhang ◽  
Mingyao Lai ◽  
Juan Li ◽  
Yangqiong Zhang ◽  
...  
Keyword(s):  
Social Status ◽  
Pediatric Patients ◽  
Picture Book ◽  
Family Satisfaction ◽  
Control Group ◽  
Play Intervention ◽  
Significant Difference ◽  
The Difference ◽  
Therapeutic Play ◽  
Experimental Group

Abstract OBJECTIVE To explore the effect of group medical games on the hospitalization adaptability of pediatric patients with neuro tumor. METHODS pediatric patients with neuro tumor (age:6 to 13 years) who were treated in hospital from June to December 2019 and were hospitalized for 1 month to 2 months. 29 pediatric patients(mean age:9y) were selected as the control group and treated as usual; 26 pediatric patients(meanage:8y) were selected as the experimental group for group therapeutic play intervention. Interventions last Monday, Wednesday and Friday of each week, with an average duration of one hour. Group medical play include: medical picture book education, medical preview game, emotional games, social table games. Two groups completed self-made questionnaires at the time of admission and two weeks after admission, including: diet, sleep, compliance, and social status, hospital adaptation and other related issues, two groups completed a satisfaction questionnaire after two weeks of admission, recorded analysis and compared the difference of questionnaire data and satisfaction of the two groups of pediatric patients. RESULTS There was no statistical difference in age and sex between the two groups, and there was no significant difference in baseline RESULTS: The re-test results showed that the experimental group was significantly better than the control group in terms of social status, hospital adaptation, compliance and family satisfaction(p＜0.05). CONCLUSION Group medical games can effectively improve the adaptability, compliance and family satisfaction of pediatric with neuro tumor.

scholarly journals Assessment of water resource security in karst area of Guizhou Province, China

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Liying Liu
Keyword(s):  
Neural Network ◽  
Water Resources ◽  
Water Resource ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Water Shortage ◽  
Guizhou Province ◽  
Karst Area ◽  
Water Resource Security ◽  
Resource Security

AbstractThis paper presents the assessment of water resource security in the Guizhou karst area, China. A mean impact value and back-propagation (MIV-BP) neural network was used to understand the influencing factors. Thirty-one indices involving five aspects, the water quality subsystem, water quantity subsystem, engineering water shortage subsystem, water resource vulnerability subsystem, and water resource carrying capacity subsystem, were selected to establish an evaluation index of water resource security. In addition, a genetic algorithm and back-propagation (GA-BP) neural network was constructed to assess the water resource security of Guizhou Province from 2001 to 2015. The results show that water resource security in Guizhou was at a moderate warning level from 2001 to 2006 and a critical safety level from 2007 to 2015, except in 2011 when a moderate warning level was reached. For protection and management of water resources in a karst area, the modes of development and utilization of water resources must be thoroughly understood, along with the impact of engineering water shortage. These results are a meaningful contribution to regional ecological restoration and socio-economic development and can promote better practices for future planning.

Study on the Generalized Holo-Factors Mathematical Model of Dimension-Error and Shape-Error for Sheet Metal in Stamping Based on the Back Propagation (BP) Neural Network

2016 ◽  
Vol 138 (6) ◽  
Author(s):  
Lizhi Gu ◽  
Tianqing Zheng
Keyword(s):  
Neural Network ◽  
Mathematical Model ◽  
Sheet Metal ◽  
Bp Neural Network ◽  
Back Propagation ◽  
Shape Error ◽  
Back Propagation Algorithm ◽  
Minor Strain ◽  
Major Strain ◽  
Forming Precision

Precision improvement in sheet metal stamping has been the concern that the stamping researchers have engaged in. In order to improve the forming precision of sheet metal in stamping, this paper devoted to establish the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping based on BP neural network. Factors influencing the forming precision of stamping sheet metal were divided, altogether ten factors, and the generalized holo-factors mathematical model of dimension-error and shape-error for sheet metal in stamping was established using the back-propagation algorithm of error based on BP neural network. The undetermined coefficients of the model previously established were soluble according to the simulation data of sheet punching combined with the specific shape based on the BP neural network. With this mathematical model, the forecast data compared with the validate data could be obtained, so as to verify the fine practicability that the previously established mathematical model had, and then, it was shown that the generalized holo-factors mathematical model of size error and shape-error had fine practicality and versatility. Based on the generalized holo-factors mathematical model of error exemplified by the cylindrical parts, a group of process parameters could be selected, in which forming thickness was between 0.713 mm and 1.335 mm, major strain was between 0.085 and 0.519, and minor strain was between −0.596 and 0.319 from the generalized holo-factors mathematical model prediction, at the same time, the forming thickness, the major strain, and the minor strain were in good condition.

Influence of Tobacco on Physical Fitness Recovery of College Students After Sports

2021 ◽  
Vol 7 (5) ◽  
pp. 3445-3451
Author(s):  
Chen Yake
Keyword(s):  
College Students ◽  
Physical Fitness ◽  
Aerobic Exercise ◽  
Negative Impact ◽  
Control Group ◽  
Exercise Time ◽  
Activity Intensity ◽  
Group I ◽  
The Difference ◽  
Experimental Group

Objectives: In this paper, the effects of tobacco on aerobic exercise ability and physical fitness recovery of college students were studied. Methods: University group sports intervention form: traditional characteristic project (basketball) + Taiji soft ball (R&D intervention project). Exercise time: 3 times/week; Activity duration: 30min; Activity intensity: the heart rate is controlled at 120-140 beats/min. All the college students in the experimental group are students who have never smoked, and the college students in the control group are students who have smoked for more than two years. The other conditions are the same. Results: The exercise time and endurance of experimental groups I and II were significantly lower than those of the control group, and the cardiopulmonary function was significantly lower than that of the control group. The indexes of experimental group II changed significantly compared with experimental group I, and the difference was statistically significant. Conclusion: Cigarette smoke can significantly reduce the aerobic exercise ability and anti fatigue ability. The longer the smoking time, the more serious the adverse effects. Therefore, tobacco smoke and nicotine will damage college students’ aerobic exercise ability and have a negative impact on the recovery of physical fitness after exercise.

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