Showing papers in "Informatics in Medicine Unlocked in 2020"

TL;DR: In this paper, an Artificial Intelligence (AI)-powered screening solution for COVID-19 infection that is deployable via a smartphone app is proposed, based on the prior work on cough-based diagnosis of respiratory diseases.

TL;DR: This paper aims to introduce a deep learning technique based on the combination of a convolutional neural network (CNN) and long short-term memory (LSTM) to diagnose COVID-19 automatically from X-ray images, which achieved desired results on the currently available dataset.

TL;DR: Several deep convolutional networks with the introduced training techniques for classifying X-ray images into three classes: normal, pneumonia, and COVID-19 are trained, and a neural network that is a concatenation of Xception and ResNet50V2 networks is proposed that achieved the best accuracy.

TL;DR: In this review, the basics of deep learning methods are discussed along with an overview of successful implementations involving image segmentation for different medical applications and the future need for further improvements is pointed out.

TL;DR: In this article, the authors proposed a new hybrid deep learning framework by combining VGG, data augmentation and spatial transformer network (STN) with CNN, which is termed as VGG Data STN with CNN (VDSNet).

TL;DR: The experimental results proved that the combination of chi-square with PCA obtains greater performance in most classifiers and the usage of PCA directly from the raw data computed lower results and would require greater dimensionality to improve the results.

TL;DR: The recent state-of-the-art methods of DR color fundus images detection and classification using deep learning techniques have been reviewed and analyzed and difference challenging issues that require more investigation are discussed.

TL;DR: The 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.

TL;DR: A model-driven architecture in the cloud, that uses deep learning algorithms in its core implementations, is used to construct models that assist in predicting skin cancer with improved accuracy.

TL;DR: An attempt has been made to suggest an in silico computational relationship between US-FDA approved drugs, plant-derived natural drugs, and Coronavirus main protease (6LU7) protein.

TL;DR: Patients in self-isolation or self-quarantine can use the new platform to send daily health symptoms and challenges to doctors via their mobile phones so that improved healthy living and a comfortable lifestyle can still be achieved even during such a problematic period of the 2019 COVID-19 pandemic.

TL;DR: The results show that heart disease risk can be predicted effectively by the proposed ensemble learning approach, which outperformed other machine learning algorithms and similar scholarly works.

TL;DR: This work aims to classify physically disabled people and Autism children's emotional expressions based on facial landmarks and electroencephalograph signals using a convolutional neural network (CNN) and long short-term memory (LSTM) classifiers by developing an algorithm for real-time emotion recognition using virtual markers through an optical flow algorithm.

TL;DR: A VGG-16 (Visual Geometry Group, also called OxfordNet) Network-based Faster Regions with Convolutional Neural Networks (Faster R-CNN) framework is introduced to detect COVID-19 patients from chest X-Ray images using an available open-source dataset.

TL;DR: The proposed ERLX is robust and can be deployed for reliable early and rapid screening of COVID-19 patients and revealed better performance when compared against existing state-of-the-art studies for the same set of features employed by them.

TL;DR: In this article, the effect of Brownian motion, thermal radiation, Schmidt number, thermophoresis, Peclet number, Magnetic field, and bioconvection Schmidt number on the desired outcomes are scrutinized.

TL;DR: The results of the ABM indicated that school and educational center closures in Urmia city, reduced the number of infected people by 4.96% each week on average and 49.61% in total from February 21 until May 10, respectively.

TL;DR: The students were satisfied with the overall shift into this collaborative e-learning environment and the new successful procedures of virtual PBL sessions, and the adoption of future online theoretical courses as well as the development of informatics computer technologies were recommended.

TL;DR: An epitope is reported, ITLCFTLKR, which is biochemically fit to HLA allelic proteins, which could be used as a potential vaccine candidate against SARS-COV-2 and can be economically beneficial and viable.

TL;DR: The experimental result shows that the proposed method improves the performance of the ANN classifier, and is more robust as compared to other methods and similar scholarly works.

TL;DR: Different aspects of novel coronavirus disease (COVID-19) are described, visualization of the spread of the infection is presented, and the potential applications of data analytics on this viral infection are discussed.

TL;DR: Clinical assessment of these three herbal compounds and hsa-miR-1307-3p may have significant outcomes for the prevention, control, and treatment of COVID-19 infection.

TL;DR: A home hospitalization system based on the Internet of Things (IoT), Fog computing, and Cloud computing, which are among the most important technologies that have contributed to the development of the healthcare sector in a significant way are proposed.

TL;DR: This work presents a multi-scale supervised 3D U- net, MSS U-Net to segment kidneys and kidney tumors from CT images, and introduces a connected-component based post processing method to enhance the performance of the overall process.

TL;DR: In this article, a total of 27 plant metabolites were screened against SARS-CoV-2 main protease proteins (MPP), Nsp9 RNA binding protein, spike receptor binding domain, spike ecto-domain and HR2 domain using a molecular docking approach.

TL;DR: Experimental analysis shows that the proposed Linguistic Neuro-Fuzzy with Feature Extraction model outperforms better as compared to other models for solving real-world problems.

TL;DR: A machine learning model is constructed to predict obstructive versus restrictive pattern, and validated using K-fold cross-validation based on ground truth data, which shows strong correlation of cough sound characteristics with airflow characteristics, which are important in identifying the type of lung diseases as either obstructive or restrictive.

TL;DR: The in silico cloning model demonstrated the efficacy of the construct vaccine along with the identified epitopes against SARS-CoV-2 and the vaccine candidate has potent efficacy against COVID-19 infection.

TL;DR: This work introduces a variable in the SEIR system of equations to study the impact of various degrees of social distancing on the spread of the disease, and demonstrates that with a stricter level of lockdowns, the COVID-19 curve can be effectively flattened in KSA.

TL;DR: Information technology was applied in several aspects, such as increasing the accuracy of diagnosis, early detection, ensuring healthcare providers’ safety, decreasing workload, saving time and cost, and drug discovery in the response phases of COVID-19.