Infectious Disease

Automated COVID-19 Classification Using Heap-Based Optimization with the Deep Transfer Learning Model.

The outbreak of the COVID-19 pandemic necessitates prompt identification of affected persons to restrict the spread of the COVID-19 epidemic. Radiological imaging such as computed tomography (CT) and chest X-rays (CXR) is considered an effective way to diagnose COVID-19. However, it needs an expert's knowledge and consumes more time. At the same time, artificial intelligence (AI) and medical image...

Multicenter Study on COVID-19 Lung Computed Tomography Segmentation with varying Glass Ground Opacities using Unseen Deep Learning Artificial Intelligence Paradigms: COVLIAS 1.0 Validation.

Variations in COVID-19 lesions such as glass ground opacities (GGO), consolidations, and crazy paving can compromise the ability of solo-deep learning (SDL) or hybrid-deep learning (HDL) artificial intelligence (AI) models in predicting automated COVID-19 lung segmentation in Computed Tomography (CT) from unseen data leading to poor clinical manifestations. As the first study of its kind, "COVLIAS...

Prediction of Potential Commercially Available Inhibitors against SARS-CoV-2 by Multi-Task Deep Learning Model.

The outbreak of COVID-19 caused millions of deaths worldwide, and the number of total infections is still rising. It is necessary to identify some pot...

HADCNet: Automatic segmentation of COVID-19 infection based on a hybrid attention dense connected network with dilated convolution.

the automatic segmentation of lung infections in CT slices provides a rapid and effective strategy for diagnosing, treating, and assessing COVID-19 ca...

Application of an EMG-Rehabilitation Robot in Patients with Post-Coronavirus Fatigue Syndrome (COVID-19)-A Feasibility Study.

This pilot study aimed to assess the safety and feasibility of an EMG-driven rehabilitation robot in patients with Post-Viral Fatigue (PVF) syndrome a...

Initial experience of a deep learning application for the differentiation of Kikuchi-Fujimoto's disease from tuberculous lymphadenitis on neck CECT.

Neck contrast-enhanced CT (CECT) is a routine tool used to evaluate patients with cervical lymphadenopathy. This study aimed to evaluate the ability o...

PK-RNN-V E: A deep learning model approach to vancomycin therapeutic drug monitoring using electronic health record data.

Vancomycin is a commonly used antimicrobial in hospitals, and therapeutic drug monitoring (TDM) is required to optimize its efficacy and avoid toxicit...

DetecDiv, a generalist deep-learning platform for automated cell division tracking and survival analysis.

Automating the extraction of meaningful temporal information from sequences of microscopy images represents a major challenge to characterize dynamica...

Clinical and bacteriological outcomes in patients with urinary tract infections presenting to primary care in Harare, Zimbabwe: a cohort study.

: Treatment for urinary tract infections (UTIs) is usually empiric and is based on local antimicrobial resistance data. These data, however, are scarc...

A Deep Learning Approach to Capture the Essence of Candida albicans Morphologies.

We present deep learning-based approaches for exploring the complex array of morphologies exhibited by the opportunistic human pathogen Candida albica...

Risk Factors Analysis of Surgical Infection Using Artificial Intelligence: A Single Center Study.

Surgical site infections (SSIs) have a major role in the evolution of medical care. Despite centuries of medical progress, the management of surgical...

Identification of missing hierarchical relations in the vaccine ontology using acquired term pairs.

BACKGROUND: The Vaccine Ontology (VO) is a biomedical ontology that standardizes vaccine annotation. Errors in VO will affect a multitude of applicati...

Novel Crow Swarm Optimization Algorithm and Selection Approach for Optimal Deep Learning COVID-19 Diagnostic Model.

Due to the COVID-19 pandemic, computerized COVID-19 diagnosis studies are proliferating. The diversity of COVID-19 models raises the questions of whic...

Application of Machine Learning in Hospitalized Patients with Severe COVID-19 Treated with Tocilizumab.

Among the IL-6 inhibitors, tocilizumab is the most widely used therapeutic option in patients with SARS-CoV-2-associated severe respiratory failure (S...

Analysis on COVID-19 Infection Spread Rate during Relief Schemes Using Graph Theory and Deep Learning.

The novel coronavirus 2019 (COVID-19) disease is a pandemic which affects thousands of people throughout the world. It has rapidly spread throughout I...

Pneumonia Detection in Chest X-Ray Images Using Enhanced Restricted Boltzmann Machine.

The process of pneumonia detection has been the focus of researchers as it has proved itself to be one of the most dangerous and life-threatening diso...

Dynamic Sepsis Prediction for Intensive Care Unit Patients Using XGBoost-Based Model With Novel Time-Dependent Features.

Sepsis is a systemic inflammatory response caused by pathogens such as bacteria. Because its pathogenesis is not clear, the clinical manifestations of...

Multistage Framework for Automatic Face Mask Detection Using Deep Learning.

The whole world is fighting as one against a deadly virus. COVID-19 cases are upon us in waves, with subsequent waves turning out to be worse than the...

Multiclass Classification for Detection of COVID-19 Infection in Chest X-Rays Using CNN.

Coronavirus took the world by surprise and caused a lot of trouble in all the important fields in life. The complexity of dealing with coronavirus lie...

An Effective Deep Learning Model for Health Monitoring and Detection of COVID-19 Infected Patients: An End-to-End Solution.

The COVID-19 infection is the greatest danger to humankind right now because of the devastation it causes to the lives of its victims. It is important...