Infectious Disease

Detection of Rare Objects by Flow Cytometry: Imaging, Cell Sorting, and Deep Learning Approaches.

Flow cytometry nowadays is among the main working instruments in modern biology paving the way for clinics to provide early, quick, and reliable diagnostics of many blood-related diseases. The major problem for clinical applications is the detection of rare pathogenic objects in patient blood. These objects can be circulating tumor cells, very rare during the early stages of cancer development, va...

Environmental Noise Classification Using Convolutional Neural Networks with Input Transform for Hearing Aids.

Hearing aids are essential for people with hearing loss, and noise estimation and classification are some of the most important technologies used in devices. This paper presents an environmental noise classification algorithm for hearing aids that uses convolutional neural networks (CNNs) and image signals transformed from sound signals. The algorithm was developed using the data of ten types of n...

Artificial Intelligence and Machine learning based prediction of resistant and susceptible mutations in Mycobacterium tuberculosis.

Tuberculosis (TB), an infectious disease caused by Mycobacterium tuberculosis (M.tb), causes highest number of deaths globally for any bacterial disea...

Machine learning for microbial identification and antimicrobial susceptibility testing on MALDI-TOF mass spectra: a systematic review.

BACKGROUND: The matrix assisted laser desorption/ionization and time-of-flight mass spectrometry (MALDI-TOF MS) technology has revolutionized the fiel...

Image analysis and artificial intelligence in infectious disease diagnostics.

BACKGROUND: Microbiologists are valued for their time-honed skills in image analysis, including identification of pathogens and inflammatory context i...

Using Artificial Intelligence to Detect COVID-19 and Community-acquired Pneumonia Based on Pulmonary CT: Evaluation of the Diagnostic Accuracy.

Background Coronavirus disease 2019 (COVID-19) has widely spread all over the world since the beginning of 2020. It is desirable to develop automatic ...

Prediction of the Vaccine-derived Poliovirus Outbreak Incidence: A Hybrid Machine Learning Approach.

Recently, significant attention has been devoted to vaccine-derived poliovirus (VDPV) surveillance due to its severe consequences. Prediction of the o...

AI-Driven Tools for Coronavirus Outbreak: Need of Active Learning and Cross-Population Train/Test Models on Multitudinal/Multimodal Data.

The novel coronavirus (COVID-19) outbreak, which was identified in late 2019, requires special attention because of its future epidemics and possible ...

Sequential classification system for recognition of malaria infection using peripheral blood cell images.

AIMS: Morphological recognition of red blood cells infected with malaria parasites is an important task in the laboratory practice. Nowadays, there is...

Artificial neural network-based models used for predicting 28- and 90-day mortality of patients with hepatitis B-associated acute-on-chronic liver failure.

BACKGROUND: This study aimed to develop prognostic models for predicting 28- and 90-day mortality rates of hepatitis B virus (HBV)-associated acute-on...

Prediction of MOF Performance in Vacuum Swing Adsorption Systems for Postcombustion CO Capture Based on Integrated Molecular Simulations, Process Optimizations, and Machine Learning Models.

Postcombustion CO capture and storage (CCS) is a key technological approach to reducing greenhouse gas emission while we transition to carbon-free ene...

Research on Multi-Time-Delay Gene Regulation Network Based on Fuzzy Label Propagation.

In view of time delay existing in gene regulation, by using the analysis idea and methods of complex network, this paper proposes a multi-time-delay g...

Automated prediction of mastitis infection patterns in dairy herds using machine learning.

Mastitis in dairy cattle is extremely costly both in economic and welfare terms and is one of the most significant drivers of antimicrobial usage in d...

Explainable decision support through the learning and visualization of preferences from a formal ontology of antibiotic treatments.

The aim of eXplainable Artificial Intelligence (XAI) is to design intelligent systems that can explain their predictions or recommendations to humans....

A generalizable 29-mRNA neural-network classifier for acute bacterial and viral infections.

Improved identification of bacterial and viral infections would reduce morbidity from sepsis, reduce antibiotic overuse, and lower healthcare costs. H...

Deep Learning from Incomplete Data: Detecting Imminent Risk of Hospital-acquired Pneumonia in ICU Patients.

Hospital acquired pneumonia (HAP) is the second most common nosocomial infection in the ICU and costs an estimated $3.1 billion annually. The ability ...

Identification of COVID-19 can be quicker through artificial intelligence framework using a mobile phone-based survey when cities and towns are under quarantine.

We propose the use of a machine learning algorithm to improve possible COVID-19 case identification more quickly using a mobile phone-based web survey...

An AI-based approach in determining the effect of meteorological factors on incidence of malaria.

This study presents the classification of malaria-prone zones based on (a) meteorological factors, (b) demographics and (c) patient information. Obser...

DeepAVP: A Dual-Channel Deep Neural Network for Identifying Variable-Length Antiviral Peptides.

Antiviral peptides (AVPs) have been experimentally verified to block virus into host cells, which have antiviral activity with decapeptide amide. Ther...

Alendronate/Vitamin D for attenuating bone mineral density loss during antiretroviral initiation: a pilot randomized controlled trial.

Antiretroviral therapy (ART) initiation is associated with decreases in bone mineral density (BMD). To plan for a larger trial, we sought to obtain p...