scholarly journals Wavelet Transform Artificial Intelligence Algorithm-Based Data Mining Technology for Norovirus Monitoring and Early Warning

2021 ◽  
Vol 2021 ◽  
pp. 1-7
Author(s):  
Xucheng Fan ◽  
Na Xue ◽  
Zhiguo Han ◽  
Chao Wang ◽  
Heer Ma ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Data Mining ◽  
Wavelet Transform ◽  
Early Warning ◽  
Ultrasound Image ◽  
Ultrasound Images ◽  
Epidemiological Analysis ◽  
Artificial Intelligence Algorithm ◽  
Stool Specimens ◽  
Monitoring And Early Warning

Norovirus monitoring and early warning can be used for diagnosis without etiological testing, and the treatment of this disease does not require the antibiotics. It often occurs in preschool children and affects their growth and development, so the coping measures for this disease are more prevention than treatment. In this study, the clinical data of 2133 children with diarrhea were collected. Based on the artificial intelligence (AI) algorithm of wavelet transform, a related model for data mining and processing of children’s intestinal ultrasound images and stool specimens was constructed. Then, the norovirus infection trend was warned based on the wavelet analysis algorithm model. The results showed that the intestinal ultrasound image processed by the wavelet transform algorithm was clearer. The positive detection rate of norovirus in children with clinical diarrhea was as high as 59%, and the children had different degrees of body damage, of which the probability of compensatory metabolic acidosis was the highest. The epidemiological analysis found that children with norovirus infection were mainly concentrated in the age group under 2 years old and over 5 years old and showed a peak of infection in December. In summary, the intelligent algorithm based on wavelet transform can realize the noise reduction of intestinal ultrasound, and it should protect children with susceptible age and susceptible seasons to reduce the clinical infection rate of norovirus.

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 Research on ultrasound image of interventional catheterization processing method based on wavelet transform and fuzzy theory

Filomat ◽  
2020 ◽  
Vol 34 (15) ◽  
pp. 5187-5194
Author(s):  
Chenyang Liang ◽  
Ning He
Keyword(s):  
Wavelet Transform ◽  
Fuzzy Theory ◽  
Ultrasound Image ◽  
Low Frequency ◽  
Speckle Noise ◽  
Ultrasound Images ◽  
Wavelet Coefficients ◽  
Image Processing Algorithm ◽  
Exponential Transformation ◽  
Interventional Catheterization

Interventional catheterization can help patients to accurately assess the condition, early diagnosis and intervention. Confirming the location of catheter by ultrasound has the advantages of real-time imaging, non-invasive, radiative, fast and convenient. Due to speckle noise and similar acoustic impedance, ultrasound images are not clear. In this paper an ultrasonic image processing algorithm based on wavelet transform and fuzzy theory is proposed. First, logarithmic transformation of ultrasound images is used to convert multiplicative noise into additive noise. Then the wavelet coefficients of the image are obtained by multiscale wavelet transform. The high frequency wavelet coefficients of the image are denoised by thresholding, and the low-frequency wavelet coefficients of the image are processed by fuzzy enhancement. Finally, the processed image is obtained through wavelet reconstruction and exponential transformation. Experiments show that this proposed method can effectively improve the visual effect of images.

scholarly journals Artificial Intelligence Pulse Coupled Neural Network Algorithm in the Diagnosis and Treatment of Severe Sepsis Complicated with Acute Kidney Injury under Ultrasound Image

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Fu Ying ◽  
Shuhua Chen ◽  
Guojun Pan ◽  
Zemin He
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Acute Kidney Injury ◽  
Severe Sepsis ◽  
Ultrasound Image ◽  
Kidney Injury ◽  
Ultrasound Images ◽  
Image Information ◽  
Ultrasonic Image ◽  
Pulse Coupled Neural Network

The objective of this study was to explore the diagnosis of severe sepsis complicated with acute kidney injury (AKI) by ultrasonic image information based on the artificial intelligence pulse coupled neural network (PCNN) algorithm. In this study, an algorithm of ultrasonic image information enhancement based on the artificial intelligence PCNN was constructed and compared with the histogram equalization algorithm and linear transformation algorithm. After that, it was applied to the ultrasonic image diagnosis of 20 cases of severe sepsis combined with AKI in hospital. The condition of each patient was diagnosed by ultrasound image performance, change of renal resistance index (RRI), ultrasound score, and receiver operator characteristic curve (ROC) analysis. It was found that the histogram distribution of this algorithm was relatively uniform, and the information of each gray level was obviously retained and enhanced, which had the best effect in this algorithm; there was a marked individual difference in the values of RRI. Overall, the values of RRI showed a slight upward trend after admission to the intensive care unit (ICU). The RRI was taken as the dependent variable, time as the fixed-effect model, and patients as the random effect; the parameter value of time was between 0.012 and 0.015, p = 0.000 < 0.05 . Besides, there was no huge difference in the ultrasonic score among different time measurements (t = 1.348 and p = 0.128 > 0.05 ). The area under the ROC curve of the RRI for the diagnosis of AKI at the 2nd day, 4th day, and 6th day was 0.758, 0.841, and 0.856, respectively, which was all greater than 0.5 ( p < 0.05 ). In conclusion, the proposed algorithm in this study could significantly enhance the amount of information in ultrasound images. In addition, the change of RRI values measured by ultrasound images based on the artificial intelligence PCNN was associated with AKI.

Data Mining Approaches to the Real-Time Monitoring and Early Warning of Convective Weather Using Lightning Data

2019 ◽  
pp. 83-101
Author(s):  
Stephan Stephany ◽  
Cesar Strauss ◽  
Alan James Peixoto Calheiros ◽  
Glauston Roberto Teixeira de Lima ◽  
João Victor Cal Garcia ◽  
...  
Keyword(s):  
Data Mining ◽  
Real Time ◽  
Early Warning ◽  
Real Time Monitoring ◽  
The Real ◽  
Convective Weather ◽  
Monitoring And Early Warning

scholarly journals Wavelet Transform-Based Ultrasound Image Enhancement Algorithm for Guided Gynecological Laparoscopy Imaging of Local Anesthetics in Perioperative Gynecological Laparoscopy

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Bingbing Sheng ◽  
Qiaoqin Yan ◽  
Xianda Zhao ◽  
Wujian Yang
Keyword(s):  
Wavelet Transform ◽  
Image Enhancement ◽  
Local Anesthetics ◽  
Ultrasound Image ◽  
Postoperative Nausea And Vomiting ◽  
Transversus Abdominis ◽  
Ultrasound Images ◽  
Tap Block ◽  
Gynecological Laparoscopy ◽  
Number Of Patients

This paper aimed to study the application of local anesthetics combined with transversus abdominis plane (TAP) block in gynecological laparoscopy (GLS) surgery during perioperative period under the guidance of ultrasound image enhanced by the wavelet transform image enhancement (WTIE) algorithm. 56 patients who underwent GLS surgery in hospital were selected and classified as the infiltrating group and block group. The puncture needle was guided by ultrasound images under WTIE algorithm, and 0.375% ropivacaine was adopted to block TAP. The results showed that the dosage of propofol in the infiltrating group (313.23 ± 19.67 mg) was remarkably inferior to the infiltrating group (377.67 ± 21.56 mg) P < 0.05 . The hospitalization time of patients in the infiltrating group (2.14 ± 0.18 days) was obviously shorter than that of the infiltrating group (3.23 ± 0.27 days) P < 0.05 . 3 h, 6 h, and 12 h after the operation, the visual analogue scores (3.82 ± 1.58 points, 2.97 ± 1.53 points, and 1.38 ± 0.57 points) of the patients in the infiltration group were considerably higher than the infiltrating group (2.31 ± 1.46 points, 1.06 ± 1.28 points, and 0.95 ± 0.43 points) P < 0.05 . 3 h, 6 h, and 12 h after the operation, the number of patients in the infiltrating group who used tramadol for salvage analgesia (2 cases, 1 case, and 1 case) was notably less than that in the infiltration group (9 cases, 7 cases, and 3 cases) P < 0.05 . In short, local anesthetics combined with TAP block can reduce postoperative VAS score and postoperative nausea and vomiting (PONV) score, which also reduced the incidence of postoperative analgesia.

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