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Radiology Radiology

Artificial Intelligence, Machine Learning, and Cardiovascular Disease.

In Clinical Medicine Insights. Cardiology

Artificial intelligence (AI)-based applications have found widespread applications in many fields of science, technology, and medicine. The use of enhanced computing power of machines in clinical medicine and diagnostics has been under exploration since the 1960s. More recently, with the advent of advances in computing, algorithms enabling machine learning, especially deep learning networks that mimic the human brain in function, there has been renewed interest to use them in clinical medicine. In cardiovascular medicine, AI-based systems have found new applications in cardiovascular imaging, cardiovascular risk prediction, and newer drug targets. This article aims to describe different AI applications including machine learning and deep learning and their applications in cardiovascular medicine. AI-based applications have enhanced our understanding of different phenotypes of heart failure and congenital heart disease. These applications have led to newer treatment strategies for different types of cardiovascular diseases, newer approach to cardiovascular drug therapy and postmarketing survey of prescription drugs. However, there are several challenges in the clinical use of AI-based applications and interpretation of the results including data privacy, poorly selected/outdated data, selection bias, and unintentional continuance of historical biases/stereotypes in the data which can lead to erroneous conclusions. Still, AI is a transformative technology and has immense potential in health care.

Mathur Pankaj, Srivastava Shweta, Xu Xiaowei, Mehta Jawahar L

2020

AI, big data, cardiovascular disease, machine learning, precision medicine

General General

The relationship between air pollution and COVID-19-related deaths: An application to three French cities.

In Applied energy ; h5-index 131.0

Being heavily dependent to oil products (mainly gasoline and diesel), the French transport sector is the main emitter of Particulate Matter (PMs) whose critical levels induce harmful health effects for urban inhabitants. We selected three major French cities (Paris, Lyon, and Marseille) to investigate the relationship between the Coronavirus Disease 19 (COVID-19) outbreak and air pollution. Using Artificial Neural Networks (ANNs) experiments, we have determined the concentration of PM2.5 and PM10 linked to COVID-19-related deaths. Our focus is on the potential effects of Particulate Matter (PM) in spreading the epidemic. The underlying hypothesis is that a pre-determined particulate concentration can foster COVID-19 and make the respiratory system more susceptible to this infection. The empirical strategy used an innovative Machine Learning (ML) methodology. In particular, through the so-called cutting technique in ANNs, we found new threshold levels of PM2.5 and PM10 connected to COVID-19: 17.4 µg/m3 (PM2.5) and 29.6 µg/m3 (PM10) for Paris; 15.6 µg/m3 (PM2.5) and 20.6 µg/m3 (PM10) for Lyon; 14.3 µg/m3 (PM2.5) and 22.04 µg/m3 (PM10) for Marseille. Interestingly, all the threshold values identified by the ANNs are higher than the limits imposed by the European Parliament. Finally, a Causal Direction from Dependency (D2C) algorithm is applied to check the consistency of our findings.

Magazzino Cosimo, Mele Marco, Schneider Nicolas

2020-Dec-01

ANNs, Artificial Neural Networks, Air pollution, Artificial neural networks, CH4, Methane, CMAQ, Community Multiscale Air Quality, CO, Carbon Monoxide, COVID-19, COVID-19, Coronavirus Disease 19, D2C, Causal Direction from Dependency, GAM, Generalized Additive Model, GHG, Greenhouse Gas, ML, Machine Learning, Machine learning, NO2, Nitrogen Dioxide, NOx, Nitrogen Oxides, O3, Ozone, PM10, Particulate Matter with an aerodynamic diameter < 10.0 µm, PM2.5, Particulate Matter with an aerodynamic diameter < 2.5 µm, Particulate matter, SO2, Sulfur Dioxide, SO3, Sulphur Trioxide, SOx, Sulphur Oxides, VOC, Volatile Organic Compounds

General General

Short-term forecasting of the coronavirus pandemic.

In International journal of forecasting

We have been publishing real-time forecasts of confirmed cases and deaths for COVID-19 from mid-March 2020 onwards, published at www.doornik.com/COVID-19. These forecasts are short-term statistical extrapolations of past and current data. They assume that the underlying trend is informative of short term developments, without requiring other assumptions of how the SARS-CoV-2 virus is spreading, or whether preventative policies are effective. As such they are complementary to forecasts from epidemiological models. The forecasts are based on extracting trends from windows of the data, applying machine learning, and then computing forecasts by applying some constraints to this flexible extracted trend. The methods have previously been applied to various other time series data and have performed well. They are also effective in this setting, providing better forecasts in the earlier stages than some epidemiological models.

Doornik Jurgen A, Castle Jennifer L, Hendry David F

2020-Sep-12

Automatic forecasting, COVID-19, Epidemiology, Forecast averaging, Forecasting, Machine learning, Smoothing, Time series, Trend indicator saturation

General General

COVID-19 detection in CT images with deep learning: A voting-based scheme and cross-datasets analysis.

In Informatics in medicine unlocked

Early detection and diagnosis are critical factors to control the COVID-19 spreading. A number of deep learning-based methodologies have been recently proposed for COVID-19 screening in CT scans as a tool to automate and help with the diagnosis. These approaches, however, suffer from at least one of the following problems: (i) they treat each CT scan slice independently and (ii) the methods are trained and tested with sets of images from the same dataset. Treating the slices independently means that the same patient may appear in the training and test sets at the same time which may produce misleading results. It also raises the question of whether the scans from the same patient should be evaluated as a group or not. Moreover, using a single dataset raises concerns about the generalization of the methods. Different datasets tend to present images of varying quality which may come from different types of CT machines reflecting the conditions of the countries and cities from where they come from. In order to address these two problems, in this work, we propose an Efficient Deep Learning Technique for the screening of COVID-19 with a voting-based approach. In this approach, the images from a given patient are classified as group in a voting system. The approach is tested in the two biggest datasets of COVID-19 CT analysis with a patient-based split. A cross dataset study is also presented to assess the robustness of the models in a more realistic scenario in which data comes from different distributions. The cross-dataset analysis has shown that the generalization power of deep learning models is far from acceptable for the task since accuracy drops from 87.68% to 56.16% on the best evaluation scenario. These results highlighted that the methods that aim at COVID-19 detection in CT-images have to improve significantly to be considered as a clinical option and larger and more diverse datasets are needed to evaluate the methods in a realistic scenario.

Silva Pedro, Luz Eduardo, Silva Guilherme, Moreira Gladston, Silva Rodrigo, Lucio Diego, Menotti David

2020

COVID-19, Chest radiography, Deep learning, EfficientNet, Pneumonia

General General

A novel solution of using deep learning for left ventricle detection: Enhanced feature extraction.

In Computer methods and programs in biomedicine

BACKGROUND AND AIM : deep learning algorithms have not been successfully used for the left ventricle (LV) detection in echocardiographic images due to overfitting and vanishing gradient descent problem. This research aims to increase accuracy and improves the processing time of the left ventricle detection process by reducing the overfitting and vanishing gradient problem.

METHODOLOGY : the proposed system consists of an enhanced deep convolutional neural network with an extra convolutional layer, and dropout layer to solve the problem of overfitting and vanishing gradient. Data augmentation was used for increasing the accuracy of feature extraction for left ventricle detection.

RESULTS : four pathological groups of datasets were used for training and evaluation of the model: heart failure without infarction, heart failure with infarction, and hypertrophy, and healthy. The proposed model provided an accuracy of 94% in left ventricle detection for all the groups compared to the other current systems. The results showed that the processing time was reduced from 0.45 s to 0.34 s in an average.

CONCLUSION : the proposed system enhances accuracy and decreases processing time in the left ventricle detection. This paper solves the issues of overfitting of the data.

Sharma Kiran, Alsadoon Abeer, Prasad P W C, Al-Dala’in Thair, Nguyen Tran Quoc Vinh, Pham Duong Thu Hang

2020-Sep-15

Convolutional neural network, Deep learning, Left ventricle, Myocardium, Normalization Feature extraction, Overfitting

General General

Skeletal bone age prediction based on a deep residual network with spatial transformer.

In Computer methods and programs in biomedicine

OBJECTIVE : Bone age prediction can be performed by medical experts manually assessment of X-ray images of the hand bone. In practice, the workload is huge, resource consumption is large, measurement takes a long time, and it is easily influenced by human factors. As such, manual estimation of bone age takes a long time and the results fluctuate greatly depending on the proficiency of the radiologist.

METHODS : The left-hand X-ray image data was identified and pre-processed. X-ray image analysis method using on deep neural network was used to automatically extract the key features of the left-hand joint bone age, and evaluation performance of the model was implemented.

RESULTS : In this paper, the deep learning method can be used to obtain the X-ray bone image features, and the convolutional neural network is used to automatically assess the age of bone. The feature region extraction method based on deep learning can extract feature information with superior performance compared to the traditional image analysis technique. Based on the residual network (ResNet) model in the deep learning algorithm, the average absolute error of the age of bones detected by the bone age assessment model is 0.455 better than traditional methods and only end-to-end deep learning methods. When the learning rate is greater than 0.0005, the MAE of Inception Resnet v2 model is higher than most models. Accuracy of bone age prediction is as high as 97.6%.

CONCLUSION : In comparison with the traditional machine learning feature extraction technique, the convolutional neural network based on feature extraction has better performance in the bone age regression model, and further improves the accuracy of image-based age of bone assessment.

Han Yaxin, Wang Guangbin

2020-Sep-12

Automatic bone age prediction, Convolutional neural network, Deep learning, Image processing, X-ray bone image