scholarly journals Development and Validation of an Arterial Pressure-Based Cardiac Output Algorithm Using a Convolutional Neural Network: Retrospective Study Based on Prospective Registry Data

10.2196/24762 ◽  
2021 ◽  
Vol 9 (8) ◽  
pp. e24762
Author(s):  
Hyun-Lim Yang ◽  
Chul-Woo Jung ◽  
Seong Mi Yang ◽  
Min-Soo Kim ◽  
Sungho Shim ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Output ◽  
Deep Learning ◽  
Retrospective Study ◽  
Arterial Pressure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Model ◽  
Learning Technique ◽  
Deep Learning Model

Background Arterial pressure-based cardiac output (APCO) is a less invasive method for estimating cardiac output without concerns about complications from the pulmonary artery catheter (PAC). However, inaccuracies of currently available APCO devices have been reported. Improvements to the algorithm by researchers are impossible, as only a subset of the algorithm has been released. Objective In this study, an open-source algorithm was developed and validated using a convolutional neural network and a transfer learning technique. Methods A retrospective study was performed using data from a prospective cohort registry of intraoperative bio-signal data from a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as the output. The model parameters were pretrained using the SV values from a commercial APCO device (Vigileo or EV1000 with the FloTrac algorithm) and adjusted with a transfer learning technique using SV values from the PAC. The performance of the model was evaluated using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with the SV values from the PAC. Results A total of 2057 surgical cases (1958 training and 99 testing cases) were used in the registry. In the deep learning model, the absolute errors of SV were 14.5 (SD 13.4) mL (10.2 [SD 8.4] mL in cardiac surgery and 17.4 [SD 15.3] mL in liver transplantation). Compared with FloTrac, the absolute errors of the deep learning model were significantly smaller (16.5 [SD 15.4] and 18.3 [SD 15.1], P<.001). Conclusions The deep learning–based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care.

scholarly journals Development and validation of an arterial pressure-based cardiac output algorithm using a convolutional neural network: Retrospective study based on prospective registry data (Preprint)

2020 ◽  
Author(s):  
Hyun-Lim Yang ◽  
Chul-Woo Jung ◽  
Seong Mi Yang ◽  
Min-Soo Kim ◽  
Sungho Shim ◽  
...  
Keyword(s):  
Neural Network ◽  
Cardiac Output ◽  
Deep Learning ◽  
Retrospective Study ◽  
Arterial Pressure ◽  
Convolutional Neural Network ◽  
Transfer Learning ◽  
Learning Model ◽  
Learning Technique ◽  
Deep Learning Model

BACKGROUND The arterial pressure-based cardiac output (APCO) is a less-invasive method for estimating the cardiac output without worries about complications from the pulmonary artery catheter (PAC). However, inaccuracies of the currently available APCO devices have been reported. Improvements of the algorithm by researchers are also impossible, since only a subset of the algorithm has been released. OBJECTIVE In this study, we developed and validated an open source APCO algorithm using convolutional neural network and the transfer learning technique. METHODS We did a retrospective study using data from a prospective cohort registry of intraoperative bio-signal data at a university hospital. The convolutional neural network model was trained using the arterial pressure waveform as input and the stroke volume (SV) value as output. The model parameters were pre-trained using the SV values from a commercial APCO device (Vigileo™ or EV1000™ with FloTrac™ algorithm) and adjusted by a transfer learning technique using SV values from the PAC. The performance of the model was evaluated by using absolute error for the PAC on the testing dataset from separate periods. Finally, we compared the performance of the deep learning model and the FloTrac with SV values from the PAC. RESULTS We used 2,057 surgical cases (1,958 training and 99 testing) in the registry for modelling. In the deep learning model, the absolute errors of SV were 14.5 ± 13.4 mL (10.2 ± 8.4 mL and 17.4 ± 15.3 in cardiac surgery and liver transplantation, respectively). In the comparison with FloTrac, the absolute errors of the deep learning model were significantly smaller than those of the FloTrac (16.5 ± 15.4 and 18.3 ± 15.1, respectively, P < .001). CONCLUSIONS The deep learning-based APCO algorithm showed better performance than the commercial APCO device. Further improvement of the algorithm developed in this study may be helpful for estimating cardiac output accurately in clinical practice and optimizing high-risk patient care. CLINICALTRIAL Not applicable.

scholarly journals A robust and interpretable, end-to-end deep learning model for cytometry data

2020 ◽  
Author(s):  
Zicheng Hu ◽  
Alice Tang ◽  
Jaiveer Singh ◽  
Sanchita Bhattacharya ◽  
Atul J. Butte
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Network Model ◽  
Neural Network Model ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Significant Information ◽  
End To End ◽  
Deep Learning Model

AbstractCytometry technologies are essential tools for immunology research, providing high-throughput measurements of the immune cells at the single-cell level. Traditional approaches in interpreting and using cytometry measurements include manual or automated gating to identify cell subsets from the cytometry data, providing highly intuitive results but may lead to significant information loss, in that additional details in measured or correlated cell signals might be missed. In this study, we propose and test a deep convolutional neural network for analyzing cytometry data in an end-to-end fashion, allowing a direct association between raw cytometry data and the clinical outcome of interest. Using nine large CyTOF studies from the open-access ImmPort database, we demonstrated that the deep convolutional neural network model can accurately diagnose the latent cytomegalovirus (CMV) in healthy individuals, even when using highly heterogeneous data from different studies. In addition, we developed a permutation-based method for interpreting the deep convolutional neural network model and identified a CD27-CD94+ CD8+ T cell population significantly associated with latent CMV infection. Finally, we provide a tutorial for creating, training and interpreting the tailored deep learning model for cytometry data using Keras and TensorFlow (github.com/hzc363/DeepLearningCyTOF).

scholarly journals Convolutional neural network for image classification based on transfer learning technique

2021 ◽  
Author(s):  
Ghassan Mohammed Halawani
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Image Classification ◽  
Transfer Learning ◽  
Learning Networks ◽  
The Neural Network ◽  
Learning Technique ◽  
Common Architecture

The main purpose of this project is to modify a convolutional neural network for image classification, based on a deep-learning framework. A transfer learning technique is used by the MATLAB interface to Alex-Net to train and modify the parameters in the last two fully connected layers of Alex-Net with a new dataset to perform classifications of thousands of images. First, the general common architecture of most neural networks and their benefits are presented. The mathematical models and the role of each part in the neural network are explained in detail. Second, different neural networks are studied in terms of architecture, application, and the working method to highlight the strengths and weaknesses of each of neural network. The final part conducts a detailed study on one of the most powerful deep-learning networks in image classification – i.e. the convolutional neural network – and how it can be modified to suit different classification tasks by using transfer learning technique in MATLAB.

scholarly journals Identification Method of Wheat Cultivars by Using a Convolutional Neural Network Combined with Images of Multiple Growth Periods of Wheat

Symmetry ◽  
2021 ◽  
Vol 13 (11) ◽  
pp. 2012
Author(s):  
Jiameng Gao ◽  
Chengzhong Liu ◽  
Junying Han ◽  
Qinglin Lu ◽  
Hengxing Wang ◽  
...  
Keyword(s):  
Neural Network ◽  
Machine Learning ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Wheat Variety ◽  
Learning Model ◽  
Wheat Cultivars ◽  
Learning Methods ◽  
Wheat Varieties ◽  
Deep Learning Model

Wheat is a very important food crop for mankind. Many new varieties are bred every year. The accurate judgment of wheat varieties can promote the development of the wheat industry and the protection of breeding property rights. Although gene analysis technology can be used to accurately determine wheat varieties, it is costly, time-consuming, and inconvenient. Traditional machine learning methods can significantly reduce the cost and time of wheat cultivars identification, but the accuracy is not high. In recent years, the relatively popular deep learning methods have further improved the accuracy on the basis of traditional machine learning, whereas it is quite difficult to continue to improve the identification accuracy after the convergence of the deep learning model. Based on the ResNet and SENet models, this paper draws on the idea of the bagging-based ensemble estimator algorithm, and proposes a deep learning model for wheat classification, CMPNet, which is coupled with the tillering period, flowering period, and seed image. This convolutional neural network (CNN) model has a symmetrical structure along the direction of the tensor flow. The model uses collected images of different types of wheat in multiple growth periods. First, it uses the transfer learning method of the ResNet-50, SE-ResNet, and SE-ResNeXt models, and then trains the collected images of 30 kinds of wheat in different growth periods. It then uses the concat layer to connect the output layers of the three models, and finally obtains the wheat classification results through the softmax function. The accuracy of wheat variety identification increased from 92.07% at the seed stage, 95.16% at the tillering stage, and 97.38% at the flowering stage to 99.51%. The model’s single inference time was only 0.0212 s. The model not only significantly improves the classification accuracy of wheat varieties, but also achieves low cost and high efficiency, which makes it a novel and important technology reference for wheat producers, managers, and law enforcement supervisors in the practice of wheat production.

scholarly journals A Novel Deep Convolutional Neural Network for Tuberculosis Detection

2021 ◽  
Vol 9 (VI) ◽  
pp. 1135-1139
Author(s):  
Syed Farhan Hyder Abidi
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Mycobacterium Tuberculosis ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Deep Convolutional Neural Network ◽  
Deep Learning Model

India accounts for the world’s largest number of cases in TB, with 2.8 million cases annually, and accounts for more than a quarter of the global TB burden. Tuberculosis (TB) is caused by the bacterium (Mycobacterium tuberculosis) which most commonly affects the lungs. TB is transmitted from person to person through the air. When people with TB cough, sneeze or spit, the germs are propelled into the air. This paper showcases a methodology which uses a Deep Learning Model (dCNN) for the detection of Tuberculosis in the lungs. The accuracy obtained by the methods for the model is desirable and dependable, which is increasingly productive in contrast to the accuracy shown by other neural networks.

Handwritten Text Recognition and Conversion Using Convolutional Neural Network (CNN) Based Deep Learning Model

2021 ◽  
Author(s):  
Jebaveerasingh Jebadurai ◽  
Immanuel Johnraja Jebadurai ◽  
Getzi Jeba Leelipushpam Paulraj ◽  
Sushen Vallabh Vangeepuram
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Text Recognition ◽  
Handwritten Text ◽  
Handwritten Text Recognition ◽  
Deep Learning Model

scholarly journals Automated Classification Analysis of Geological Structures Based on Images Data and Deep Learning Model

2018 ◽  
Vol 8 (12) ◽  
pp. 2493 ◽  
Author(s):  
Ye Zhang ◽  
Gang Wang ◽  
Mingchao Li ◽  
Shuai Han
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Transfer Learning ◽  
Poor Performance ◽  
Learning Model ◽  
Geological Structure ◽  
Gradient Boosting ◽  
Geological Structures ◽  
Extreme Gradient Boosting ◽  
Deep Learning Model

It is meaningful to study the geological structures exposed on the Earth’s surface, which is paramount to engineering design and construction. In this research, we used 2206 images with 12 labels to identify geological structures based on the Inception-v3 model. Grayscale and color images were adopted in the model. A convolutional neural network (CNN) model was also built in this research. Meanwhile, K nearest neighbors (KNN), artificial neural network (ANN) and extreme gradient boosting (XGBoost) were applied in geological structures classification based on features extracted by the Open Source Computer Vision Library (OpenCV). Finally, the performances of the five methods were compared and the results indicated that KNN, ANN, and XGBoost had a poor performance, with the accuracy of less than 40.0%. CNN was overfitting. The model trained using transfer learning had a significant effect on a small dataset of geological structure images; and the top-1 and top-3 accuracy of the model reached 83.3% and 90.0%, respectively. This shows that texture is the key feature in this research. Transfer learning based on a deep learning model can extract features of small geological structure data effectively, and it is robust in geological structure image classification.

scholarly journals A Deep Learning Based Prediction of Arabic Manuscripts Handwriting Style

2020 ◽  
Vol 17 (5) ◽  
pp. 702-712
Author(s):  
Manal Khayyat ◽  
Lamiaa Elrefaei
Keyword(s):  
Neural Network ◽  
Deep Learning ◽  
Convolutional Neural Network ◽  
Learning Model ◽  
Evaluation Metrics ◽  
The Internet ◽  
Promising Technology ◽  
Deep Learning Model

With the increasing amounts of existing unorganized images on the internet today and the necessity to use them efficiently in various types of applications. There is a critical need to discover rigid models that can classify and predict images successfully and instantaneously. Therefore, this study aims to collect Arabic manuscripts images in a dataset and predict their handwriting styles using the most powerful and trending technologies. There are many types of Arabic handwriting styles, including Al-Reqaa, Al-Nask, Al-Thulth, Al-Kufi, Al-Hur, Al-Diwani, Al-Farsi, Al-Ejaza, Al-Maghrabi, Al-Taqraa, etc. However, the study classified the collected dataset images according to the handwriting styles and focused on only six types of handwriting styles that existed in the collected Arabic manuscripts. To reach our goal, we applied the MobileNet pre-trained deep learning model on our classified dataset images to automatically capture and extract the features from them. Afterward, we evaluated the performance of the developed model by computing its recorded evaluation metrics. We reached that MobileNet convolutional neural network is a promising technology since it reached 0.9583 as the highest recorded accuracy and 0.9633 as the average F-score

Sign in / Sign up

Export Citation Format

Share Document