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 Imaging Features of Artificial Intelligence Algorithm in the Analysis of Cerebral Protective Effect of Craniotomy Hematoma Removal under Propofol Anesthesia in Patients with Chronic Subdural Hematoma

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Manyun Bai ◽  
Yufang Li ◽  
Qian Zhao ◽  
Renzhong Guo
Keyword(s):  
Artificial Intelligence ◽  
Protective Effect ◽  
Subdural Hematoma ◽  
Chronic Subdural Hematoma ◽  
Ct Images ◽  
Control Group ◽  
Brain Ct ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm ◽  
Experimental Group

Objective. The aim of this work was to study the cerebral protective effect of craniotomy hematoma removal under propofol anesthesia based on the artificial intelligence algorithm analysis of the changes in imaging characteristics of chronic subdural hematoma (CSDH) patients. Methods. A total of 60 CSDH patients who were treated in hospital were recruited and were randomly rolled into an experimental group and a control group, with 30 people in each group. Patients in the experimental group were treated with propofol anesthesia + craniotomy hematoma removal, while those in the control group were treated with conventional anesthesia + craniotomy hematoma removal. Head CT examinations were performed on the next day, one week, one month, three months, and six months after the operation. A two-dimensional empirical mode decomposition (BEMD) algorithm was used for edge detection and denoising of brain CT images of CSDH patients. Then, the amount of hematoma was calculated, and the Markwalder grading was performed to evaluate the neurological function. The number of recurrence and reoperation cases within six months of follow-up was collected. Results. 1. The quality of CT images was remarkably improved after processing with artificial intelligence algorithms. 2. The amount of hematoma in the experimental group was remarkably lower than that in the control group at January, March, and June after surgery (12.89 ± 2.12 VS 20.32 ± 16.41; 7.55 ± 4.13 VS 15.88 ± 14.22; 3.39 ± 3.79 VS 6.55 ± 3.69, P < 0.05 ). 3. The experimental group was remarkably better than the control group in Markwalder grading three months and six months after the operation ( P < 0.05 ). Conclusion. Artificial intelligence algorithm had good effect on the brain CT image processing of CSDH patients, and craniotomy hematoma removal under propofol anesthesia had an ideal brain protection effect.

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 X-Ray Film under Artificial Intelligence Algorithm in the Evaluation for Nursing Effect of Gamma Nail Internal Fixation in Elderly Patients with Intertrochanteric Fracture of Femur

2021 ◽  
Vol 2021 ◽  
pp. 1-11
Author(s):  
Yudi Wang ◽  
Xin Wang ◽  
Lei Jin ◽  
Xuemei Wei
Keyword(s):  
Artificial Intelligence ◽  
Elderly Patients ◽  
Internal Fixation ◽  
Intertrochanteric Fracture ◽  
Gamma Nail ◽  
Control Group ◽  
Intertrochanteric Fractures ◽  
X Ray ◽  
Intelligence Algorithm ◽  
Artificial Intelligence Algorithm

The aim of this work was to explore the effects of Gamma nail internal fixation for intertrochanteric fracture of femur by X-ray film classification and recognition method based on artificial intelligence algorithm. The study subjects were 100 elderly patients with intertrochanteric fracture of femur admitted to hospital. The cases were diagnosed as elderly (over 60 years old) femoral intertrochanteric fractures by X-ray or CT. They were divided into two groups, with 50 persons in each group: one group used the X-ray film evaluation image guidance based on the artificial intelligence algorithm (research group), and the other group did not use algorithmic guidance (control group). The results showed that the segmentation effect of the proposed algorithm was similar to the gold standard segmentation result, indicating that the algorithm was effective and feasible in the segmentation of fractures and bones. The global level set algorithm was set as the control. The ultimate measurement accuracy (UMA) value of the algorithm group was ( 1.77 ± 0.22 ), and the UMA value of the global level set algorithm group was ( 3.42 ± 0.36 ), indicating that the image processed by the algorithm group had obvious numerical effect, high accuracy, and good retention of details. The operation time, intraoperative blood loss, incision length, hospital stay, weight-bearing time, and fracture healing time of the two groups were all better than those of the control group. One month after surgery, the Harris score of the algorithm group was 67, and that of the control group was 51, with a 16-point difference between the two groups ( p < 0.05 ). The patient had less pain and fast recovery speed, indicating that it was a good way to treat elderly intertrochanteric fractures with the nursing effect of X-ray Gamma nail internal fixation based on an artificial intelligence algorithm. The artificial intelligence algorithm not only can be applied to the Gamma nail internal fixation of elderly patients with intertrochanteric fractures but also can be applied to the X-ray image processing of other fractures and other surgical methods to provide effective treatment for fracture patients.

scholarly journals Prediction of velocity profile of water based copper nanofluid in a heated porous tube using CFD and genetic algorithm

2021 ◽  
Vol 11 (1) ◽  
Author(s):  
Tiziana Ciano ◽  
Massimiliano Ferrara ◽  
Meisam Babanezhad ◽  
Afrasyab Khan ◽  
Azam Marjani
Keyword(s):  
Artificial Intelligence ◽  
Genetic Algorithm ◽  
Velocity Profile ◽  
Fluid Velocity ◽  
Accurate Prediction ◽  
Inference System ◽  
Porous Tube ◽  
Intelligence Algorithm ◽  
Water Based ◽  
Artificial Intelligence Algorithm

AbstractThe heat transfer improvements by simultaneous usage of the nanofluids and metallic porous foams are still an attractive research area. The Computational fluid dynamics (CFD) methods are widely used for thermal and hydrodynamic investigations of the nanofluids flow inside the porous media. Almost all studies dedicated to the accurate prediction of the CFD approach. However, there are not sufficient investigations on the CFD approach optimization. The mesh increment in the CFD approach is one of the challenging concepts especially in turbulent flows and complex geometries. This study, for the first time, introduces a type of artificial intelligence algorithm (AIA) as a supplementary tool for helping the CFD. According to the idea of this study, the CFD simulation is done for a case with low mesh density. The artificial intelligence algorithm uses learns the CFD driven data. After the intelligence achievement, the AIA could predict the fluid parameters for the infinite number of nodes or dense mesh without any limitations. So, there is no need to solve the CFD models for further nodes. This study is specifically focused on the genetic algorithm-based fuzzy inference system (GAFIS) to predict the velocity profile of the water-based copper nanofluid turbulent flow in a porous tube. The most intelligent GAFIS could perform the most accurate prediction of the velocity. Hence, the intelligence of GAFIS is tested for different values of cluster influence range (CIR), squash factor(SF), accept ratio (AR) and reject ratio (RR), the population size (PS), and the percentage of crossover (PC). The maximum coefficient of determination (~ 0.97) was related to the PS of 30, the AR of 0.6, the PC of 0.4, CIR of 0.15, the SF 1.15, and the RR of 0.05. The GAFIS prediction of the fluid velocity was in great agreement with the CFD. In the most intelligent condition, the velocity profile predicted by GAFIS was similar to the CFD. The nodes increment from 537 to 7671 was made by the GAFIS. The new predictions of the GAFIS covered all CFD results.

scholarly journals Deformation Prediction of Unstable Slopes Based on Real-Time Monitoring and DeepAR Model

Sensors ◽  
2020 ◽  
Vol 21 (1) ◽  
pp. 14
Author(s):  
Mei Dong ◽  
Hongyu Wu ◽  
Hui Hu ◽  
Rafig Azzam ◽  
Liang Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Real Time ◽  
Monitoring System ◽  
Slope Instability ◽  
Mountainous Area ◽  
Probabilistic Forecasting ◽  
Intelligence Algorithm ◽  
Good Prediction Accuracy ◽  
Artificial Intelligence Algorithm ◽  
Steep Slopes

With increased urbanization, accidents related to slope instability are frequently encountered in construction sites. The deformation and failure mechanism of a landslide is a complex dynamic process, which seriously threatens people’s lives and property. Currently, prediction and early warning of a landslide can be effectively performed by using Internet of Things (IoT) technology to monitor the landslide deformation in real time and an artificial intelligence algorithm to predict the deformation trend. However, if a slope failure occurs during the construction period, the builders and decision-makers find it challenging to effectively apply IoT technology to monitor the emergency and assist in proposing treatment measures. Moreover, for projects during operation (e.g., a motorway in a mountainous area), no recognized artificial intelligence algorithm exists that can forecast the deformation of steep slopes using the huge data obtained from monitoring devices. In this context, this paper introduces a real-time wireless monitoring system with multiple sensors for retrieving high-frequency overall data that can describe the deformation feature of steep slopes. The system was installed in the Qili connecting line of a motorway in Zhejiang Province, China, to provide a technical support for the design and implementation of safety solutions for the steep slopes. Most of the devices were retained to monitor the slopes even after construction. The machine learning Probabilistic Forecasting with Autoregressive Recurrent Networks (DeepAR) model based on time series and probabilistic forecasting was introduced into the project to predict the slope displacement. The predictive accuracy of the DeepAR model was verified by the mean absolute error, the root mean square error and the goodness of fit. This study demonstrates that the presented monitoring system and the introduced predictive model had good safety control ability during construction and good prediction accuracy during operation. The proposed approach will be helpful to assess the safety of excavated slopes before constructing new infrastructures.

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