scholarly journals Prediction of exercise sudden death in rabbit exhaustive swimming using deep neural network

2021 ◽  
Vol 20 (1) ◽  
Author(s):  
Yao Zhang ◽  
Yineng Zheng ◽  
Menglu Wang ◽  
Xingming Guo
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
New Zealand ◽  
Sudden Death ◽  
Weight Bearing ◽  
Left Ventricular ◽  
Threshold Method ◽  
Time Points ◽  
Cardiac Fatigue ◽  
New Zealand Rabbits

Abstract Background and objective Moderate exercise contributes to good health. However, excessive exercise may lead to cardiac fatigue, myocardial damage and even exercise sudden death. Monitoring the heart health has important implication to prevent exercise sudden death. Diagnosis methods such as electrocardiogram, echocardiogram, blood pressure and histological analysis have shown that arrhythmia and left ventricular fibrosis are early warning symptoms of exercise sudden death. Heart sounds (HS) can reflect the changes of cardiac valve, cardiac blood flow and myocardial function. Deep learning has drawn wide attention because of its ability to recognize disease. Therefore, a deep learning method combined with HS was proposed to predict exercise sudden death in New Zealand rabbits. The objective is to develop a method to predict exercise sudden death in New Zealand rabbits. Methods This paper proposed a method to predict exercise sudden death in New Zealand rabbits based on convolutional neural network (CNN) and gated recurrent unit (GRU). The weight-bearing exhaustive swimming experiment was conducted to obtain the HS of exercise sudden death and surviving New Zealand rabbits (n = 11/10) at four different time points. Then, the improved Viola integral method and double threshold method were employed to segment HS signals. The segmented HS frames at different time points were taken as the input of a combined CNN and GRU called CNN–GRU network to complete the prediction of exercise sudden death. Results In order to evaluate the performance of proposed network, CNN and GRU were used for comparison. When the fourth time point segmented HS frames were taken as input, the result shows that the proposed network has better performance with an accuracy of 89.57%, a sensitivity of 89.38% and a specificity of 92.20%. In addition, the segmented HS frames at different time points were input into CNN–GRU network, and the result shows that with the progress of the experiment, the prediction accuracy of exercise sudden death in New Zealand rabbits increased from 50.98 to 89.57%. Conclusion The proposed network shows good performance in classifying HS, which proves the feasibility of deep learning in exploring exercise sudden death. Further, it may have important implications in helping humans explore exercise sudden death.

scholarly journals Automated Classification of Osteoarthritis in the Knee Using Deep Learning

2021 ◽  
Author(s):  
Simon Olsson ◽  
Ehsan Akbarian ◽  
Anna Lind ◽  
Ali Sharif Razavian ◽  
Max Gordon
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Weight Bearing ◽  
Operative Management ◽  
University Hospital ◽  
Automated Classification ◽  
Knee Oa ◽  
Orthopedic Surgeons ◽  
Grading Scale ◽  
Visual Disturbances

Abstract BACKGROUNDPrevalence for knee osteoarthritis is rising in Sweden and globally due to an ageing and more obese population. This has subsequently led to an increasing demand for knee arthroplasties. Correctly diagnosing, classifying, follow-up and planning for either conservative or operative management of knee OA is therefore of a great interest. Most orthopedic surgeons rely on standard weight bearing radiographs, improving the reliability and reproducibility of these interpretations could thus be hugely beneficial. Recently deep learning, a form of artificial intelligence (AI), has shown promising results for interpreting radiographs. In this study, we aim to evaluate how well an AI can classify knee OA severity using entire image series and not excluding common visual disturbances such as implants, casts and other pathologies.MethodsWe selected 6103 radiograph exams taken at Danderyd University Hospital between the years 2002-2016 and manually categorized them according to the Kellgren & Lawrence grading scale (KL). We then trained a convolutional neural network that we evaluated against a test set of 300 exams. These exams had been reviewed independently by two senior orthopedic surgeons who settled exams with disagreement through a consensus session. ResultsOur network yielded an overall high AUC of >0.87 for all KL grades except KL grade 2 and a mean AUC of 0.92. When merging adjacent KL grades together, all but one group showed near perfect results with AUC > 0.95 indicating excellent performance. ConclusionWe found that we could teach a neural network classify knee OA severity and laterality using the KL grading scale without cleaning the input data from major visual disturbances such as implants and other pathologies.

scholarly journals Age, Sex and Body Weight Related Echocardiographic Parameters of Clinically Healthy New Zealand White Rabbits

2019 ◽  
Vol 47 (1) ◽  
Author(s):  
Omer Gurkan Dilek ◽  
Hasan Erden
Keyword(s):  
Body Weight ◽  
New Zealand ◽  
Leading Edge ◽  
Left Ventricular ◽  
Cardiovascular Research ◽  
New Zealand White Rabbits ◽  
Echocardiographic Examination ◽  
Echocardiographic Parameters ◽  
Fractional Shortening ◽  
New Zealand Rabbits

Background: Echocardiography provides useful additional information on cardiac dimensions including heart wall and dimension of the ventricles, atria and conditions. Rabbits have been diagnosed with cardiac diseases, and an understanding of the animals’ cardiac chamber dimensions is vital in assessing the diseases’ severity and prognoses. Changes in cardiac dimensions due to age related and body weight were determined at different animals. The increased incidence of congenital cardiovascular anomalies makes it essential to establish the diagnosis at an early age. The aims of this study 1) establish normal values for the commonly used two-dimensional (2D) M-mode parameters using pre anaesthetics and 2) evaluate the effects of age, sex, and body weight on echocardiographic parameters in clinically healthy New Zealand rabbits.Materials, Methods & Results: In this study we used 49 New Zealand white rabbits (25 male, 24 female) all reared under the same conditions. A general physical and clinical examination including complete blood count was performed for each animal. Body surface area was calculated as BSA= 0.00718 × Height 0.725 × Weight 0.425. Rabbits were sedated with midazolam before echocardiographic examination was performed. Echocardiographic examination was performed using a DC 6-Vet® (Mindray, PRC) ultrasonographic device equipped with a micro-convex 8 MHz probe. Right parasternal short-axis view, B-mode and two-dimensional guided M-mode parameters were measured. Echocardiographic measurements were performed using leading-edge-to-leading-edge conventions outlined by the American Society of Echocardiography. Statistical analyses were employed using the SPSS 19.0 program. Sex had no significant effect on the measured echocardiographic parameters except in the case of interventricular septum thickness in diastole values of the three-month-old rabbits. The increase in the left ventricular systolic and diastolic diameters, E- point to septal separation, diastolic aortic root parameters for the three, six, and nine-month-old groups indicated persistent anatomic heart enlargement. However, it was also discovered that the fractional shortening percentage of the left ventricle and diastolic diameters of left atrium:aortic root  were unrelated to age and weight.Discussion: New Zealand rabbits are an important model for cardiovascular research, mainly as they are small and relatively inexpensive however large enough to allow anatomical and physiological experiments. Different ages and sizes of New Zealand rabbits showed no significant difference in fractional shortening despite heart enlargement. The ejection fraciton, which can be calculated from the left ventricular dimensions via M-mode echocardiography, is another parameter of cardiac function. Our echocardiograph calculated the ejection fraction automatically using the Teicholz formula. It was further observed that three-month-old rabbits ejection fraction of the left ventricle was higher than that of the six and nine-month-old rabbits. This might indicate the necessity of periodical analysis of echocardiographic parameters. Transthoracic echocardiography can be considered an applicable method for cardiovascular research using a growing rabbit animal model after appropriate adjustments for age, sex and body size. These findings give a better understanding of the functional changes investigated by echocardiography in rabbits and will be helpful when evaluating echocardiographic data in small experimental animals.

Developing an Artificial Intelligence Project in your Radiology Department

2020 ◽  
Vol 2 ◽  
pp. 58-61 ◽  
Author(s):  
Syed Junaid ◽  
Asad Saeed ◽  
Zeili Yang ◽  
Thomas Micic ◽  
Rajesh Botchu
Keyword(s):  
Neural Network ◽  
Artificial Intelligence ◽  
Health Care ◽  
Deep Learning ◽  
Learning Algorithms ◽  
Radiology Department ◽  
Short Article ◽  
Computing Power ◽  
Road Map ◽  
Key Concepts

The advances in deep learning algorithms, exponential computing power, and availability of digital patient data like never before have led to the wave of interest and investment in artificial intelligence in health care. No radiology conference is complete without a substantial dedication to AI. Many radiology departments are keen to get involved but are unsure of where and how to begin. This short article provides a simple road map to aid departments to get involved with the technology, demystify key concepts, and pique an interest in the field. We have broken down the journey into seven steps; problem, team, data, kit, neural network, validation, and governance.

Convolutional Neural Network of Atomic Surface Structures to Predict Binding Energies for High-Throughput Screening of Catalysts

2019 ◽  
Author(s):  
Seoin Back ◽  
Junwoong Yoon ◽  
Nianhan Tian ◽  
Wen Zhong ◽  
Kevin Tran ◽  
...  
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
High Throughput ◽  
High Throughput Screening ◽  
Binding Energies ◽  
Surface Structures ◽  
Voronoi Polyhedra ◽  
Atomic Surface

We present an application of deep-learning convolutional neural network of atomic surface structures using atomic and Voronoi polyhedra-based neighbor information to predict adsorbate binding energies for the application in catalysis.

Antiurolithiatic effect of Cymbopogon Proximus, Alhagi Maurorum, on Sulfadimidine induced urolithiasis in male New Zealand rabbits

2019 ◽  
Vol 20 (1) ◽  
pp. 12-18
Author(s):  
Sameh El-Nabtity
Keyword(s):  
New Zealand ◽  
Intramuscular Injection ◽  
Negative Control Group ◽  
Negative Control ◽  
Control Group ◽  
Aqueous Extracts ◽  
Blood And Urine Samples ◽  
Group 2 ◽  
New Zealand Rabbits ◽  
Group 1

The present study aimed to investigate the prophylactic effect of Cymbopogon proximus and Alhagi maurorum on Sulfadimidine induced urolithiasis in rabbits . Thirty New Zealand male rabbits were allocated into six equal groups (each of five): Group (1) was used as a negative control. Group(2) were administered sulfadimidine (200mg/kg) by intramuscular injection.Groups(3) and (4) were administered sulfadimidine(200mg/kg) by intramuscular injection and 330mg/kg of Cymbopogon proximus alcoholic and aqueous extracts respectively orally.Groups(5) and (6) were administered sulfadimidine(200mg/kg) by intramuscular injection and 400mg/kg of Alhagi maurorum alcoholic and aqueous extracts respectively orally. The period of experiment was 10 days. Blood and urine samples were collected from rabbits on the 10th day. The results recorded a significant decrease in serum creatinine, urea, uric acid and crystalluria in Cymbopogon proximus and Alhagi maurorum groups compared to sulfadimidine treated group.We conclude that Cymbopogon proximus and Alhagi maurorum have a nephroprotective and antiurolithiatic effects against sulfadimidine induced crystalluria.

Improving Sentiment Analysis using Hybrid Deep Learning Model

2020 ◽  
Vol 13 (4) ◽  
pp. 627-640 ◽  
Author(s):  
Avinash Chandra Pandey ◽  
Dharmveer Singh Rajpoot
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Sentiment Analysis ◽  
Classification Accuracy ◽  
Short Term Memory ◽  
Computational Cost ◽  
Extraction Process ◽  
Learning Model ◽  
Sentiment Classification ◽  
Deep Learning Model

Background: Sentiment analysis is a contextual mining of text which determines viewpoint of users with respect to some sentimental topics commonly present at social networking websites. Twitter is one of the social sites where people express their opinion about any topic in the form of tweets. These tweets can be examined using various sentiment classification methods to find the opinion of users. Traditional sentiment analysis methods use manually extracted features for opinion classification. The manual feature extraction process is a complicated task since it requires predefined sentiment lexicons. On the other hand, deep learning methods automatically extract relevant features from data hence; they provide better performance and richer representation competency than the traditional methods. Objective: The main aim of this paper is to enhance the sentiment classification accuracy and to reduce the computational cost. Method: To achieve the objective, a hybrid deep learning model, based on convolution neural network and bi-directional long-short term memory neural network has been introduced. Results: The proposed sentiment classification method achieves the highest accuracy for the most of the datasets. Further, from the statistical analysis efficacy of the proposed method has been validated. Conclusion: Sentiment classification accuracy can be improved by creating veracious hybrid models. Moreover, performance can also be enhanced by tuning the hyper parameters of deep leaning models.

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