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 Monitoring of Neuroendocrine Changes in Acute Stage of Severe Craniocerebral Injury by Transcranial Doppler Ultrasound Image Features Based on Artificial Intelligence Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-9
Author(s):  
Tao Wang ◽  
Yizhu Chen ◽  
Hangxiang Du ◽  
Yongan Liu ◽  
Lidi Zhang ◽  
...  
Keyword(s):  
Artificial Intelligence ◽  
Doppler Ultrasound ◽  
Acceptance Rate ◽  
Segmentation Algorithm ◽  
Control Group ◽  
Imaging Diagnosis ◽  
Doppler Ultrasound Imaging ◽  
Image Area ◽  
Artificial Intelligence Algorithm ◽  
Experimental Group

This study was aimed at exploring the application value of transcranial Doppler (TCD) based on artificial intelligence algorithm in monitoring the neuroendocrine changes in patients with severe head injury in the acute phase; 80 patients with severe brain injury were included in this study as the study subjects, and they were randomly divided into the control group (conventional TCD) and the experimental group (algorithm-optimized TCD), 40 patients in each group. An artificial intelligence neighborhood segmentation algorithm for TCD images was designed to comprehensively evaluate the application value of this algorithm by measuring the TCD image area segmentation error and running time of this algorithm. In addition, the Glasgow coma scale (GCS) and each neuroendocrine hormone level were used to assess the neuroendocrine status of the patients. The results showed that the running time of the artificial intelligence neighborhood segmentation algorithm for TCD was 3.14 ± 1.02   s , which was significantly shorter than 32.23 ± 9.56   s of traditional convolutional neural network (CNN) algorithms ( P < 0.05 ). The false rejection rate (FRR) of TCD image area segmentation of this algorithm was significantly reduced, and the false acceptance rate (FAR) and true acceptance rate (TAR) were significantly increased ( P < 0.05 ). The consistent rate of the GCS score and Doppler ultrasound imaging diagnosis results in the experimental group was 93.8%, which was significantly higher than the 80.3% in the control group ( P < 0.05 ). The consistency rate of Doppler ultrasound imaging diagnosis results of patients in the experimental group with abnormal levels of follicle stimulating hormone (FSH), prolactin (PRL), growth hormone (GH), adrenocorticotropic hormone (ACTH), and thyroid stimulating hormone (TSH) was significantly higher than that of the control group ( P < 0.05 ). In summary, the artificial intelligence neighborhood segmentation algorithm can significantly shorten the processing time of the TCD image and reduce the segmentation error of the image area, which significantly improves the monitoring level of TCD for patients with severe craniocerebral injury and has good clinical application value.

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 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 Computed Tomography Image Characteristics before and after Interventional Treatment of Children’s Lymphangioma under Artificial Intelligence Algorithm

2021 ◽  
Vol 2021 ◽  
pp. 1-8
Author(s):  
Chuangao Yin ◽  
Song Wang ◽  
Deng Pan
Keyword(s):  
Artificial Intelligence ◽  
Computed Tomography ◽  
Effective Treatment ◽  
Interventional Therapy ◽  
Ct Images ◽  
Ct Imaging ◽  
Interventional Treatment ◽  
Intelligence Algorithm ◽  
Before And After ◽  
Artificial Intelligence Algorithm

The artificial intelligence algorithm was used to analyze the characteristics of computed tomography (CT) images before and after interventional treatment of children’s lymphangioma. Retrospective analysis was performed, and 30 children with lymphangioma from the hospital were recruited as the study subjects. The ultrasound-guided bleomycin interventional therapy was adopted and applied to CT scanning through convolutional neural network (CNN). The CT imaging-related indicators before and after interventional therapy were detected, and feature analysis was performed. In addition, the CNN algorithm was adopted to segment the image of the tumor was clearer and more accurate. At the same time, the Dice similarity coefficient (DSC) of the CNN algorithm was 0.9, which had a higher degree of agreement. In terms of clinical symptoms, the cured children’s lesions disappeared, the skin surface returned to normal color, and the treatment was smooth. In the two cases with effective treatment, the cystic mass at the lesion site was significantly smaller, and the nodules disappeared. CT images before interventional therapy showed that lymphangiomas in children were more common in the neck. The cystic masses at all lesion sites varied in diameter and size, and most of them were similar to round and irregular, with uniform density distribution. The boundary was clear, the cyst was solid, and there were different degrees of compression and spread to the surrounding structure. Most of them were polycystic, and a few of them were single cystic. After interventional treatment, CT images showed that 27 cases of cured children’s lymphangioma completely disappeared. Lymphangioma was significantly reduced in two children with effective treatment. Edema around the tumor also decreased significantly. Patients who did not respond to the treatment received interventional treatment again, and the tumors disappeared completely on CT imaging. No recurrence or new occurrence was found in three-month follow-up. The total effective rate of interventional therapy for lymphangioma in children was 96.67%. The CNN algorithm can effectively compare the CT image features before and after interventional treatment for children’s lymphangioma. It was suggested that the artificial intelligence algorithm-aided CT imaging examination was helpful to guide physicians in the accurate treatment of children’s lymphangioma.

scholarly journals Risk of recurrence of subdural hematoma after EMMA vs surgical drainage – Systematic review and meta-analysis

2021 ◽  
pp. 159101992199096
Author(s):  
Joshua Dian ◽  
Janice Linton ◽  
Jai JS Shankar
Keyword(s):  
Confidence Interval ◽  
Subdural Hematoma ◽  
Meta Analysis ◽  
Chronic Subdural Hematoma ◽  
Middle Meningeal Artery ◽  
Control Group ◽  
Complication Rates ◽  
Recurrence Rates ◽  
Invasive Approach ◽  
Risk Of Recurrence

Objective Chronic subdural hematoma (CSDH) is a common and debilitating neurological condition whose treatments, including burr hole drainage and craniotomy, suffer from high rates of recurrence and complication. Embolization of the middle meningeal artery (EMMA) is a promising minimally invasive approach to manage CSDH in a broad set of patients. Methods To evaluate the efficacy and safety of EMMA, a database search was conducted including the terms “subdural hematoma; embolization; embolized; middle meningeal” was performed and yielded a total of 260 results. Following exclusion based on predefined criteria, a total of four studies were identified and outcomes including recurrence rates and complication rates were extracted for analysis. Results Four studies including intervention and control groups were included with a total of n = 888 patients. The relative risk of CSDH recurrence in the EMMA (3.5%) compared to control group (23.5%) was significantly reduced when EMMA was performed (risk ratio = 0.17; 95% confidence interval (CI) 0.05–0.67). In addition, rates of complication were not significantly different between patients with conventional therapy and those who received EMMA (OR = 0.77; 95 confidence interval (CI) 0.3–1.99). Conclusion Based on limited data, EMMA reduces the risk of recurrence by 20% compared to surgical treatment for CSDH.

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.

Sign in / Sign up

Export Citation Format

Share Document