COVID-19

Plant disease identification using contextual mask auto-encoder optimized with dynamic differential annealed optimization algorithm.

Most of the food consumed worldwide is produced by plants. Plant disease is a major cause of reduced production, but can be managed with regular monitoring. Manually observing plant diseases takes more time and is error-prone. Early detection of plant diseases with the aid of artificial intelligence and computer vision can decrease the effects of disease and help plants withstand the downsides of ...

Automated Deep Learning-Based Diagnosis and Molecular Characterization of Acute Myeloid Leukemia Using Flow Cytometry.

The current flow cytometric analysis of blood and bone marrow samples for diagnosis of acute myeloid leukemia (AML) relies heavily on manual intervention in the processing and analysis steps, introducing significant subjectivity into resulting diagnoses and necessitating highly trained personnel. Furthermore, concurrent molecular characterization via cytogenetics and targeted sequencing can take m...

Predicting multiple linear stapler firings in double stapling technique with an MRI-based deep-learning model.

Multiple linear stapler firings is a risk factor for anastomotic leakage (AL) in laparoscopic low anterior resection (LAR) using double stapling techn...

Mapping the flow of knowledge as guidance for ethics implementation in medical AI: A qualitative study.

In response to the COVID-19 crisis, Artificial Intelligence (AI) has been applied to a range of applications in healthcare and public health such as c...

Preclinical efficacy of a cell division protein candidate gonococcal vaccine identified by artificial intelligence.

Vaccines to curb the global spread of multidrug-resistant gonorrhea are urgently needed. Here, 26 vaccine candidates identified by an artificial intel...

MultiCOVID: a multi modal deep learning approach for COVID-19 diagnosis.

The rapid spread of the severe acute respiratory syndrome coronavirus 2 led to a global overextension of healthcare. Both Chest X-rays (CXR) and blood...

Digenic Analysis Finds Highly Interactive Genetic Variants Underlying Polygenic Traits.

We briefly review our recently published approach to mining digenic genotype patterns, which consist of two genotypes each originating in a different ...

Reservoir computing models based on spiking neural P systems for time series classification.

Nonlinear spiking neural P (NSNP) systems are neural-like membrane computing models with nonlinear spiking mechanisms. Because of this nonlinear spiki...

A White-Box Testing for Deep Neural Networks Based on Neuron Coverage.

With the introduction of neuron coverage as a testing criterion for deep neural networks (DNNs), covering more neurons to detect more internal logic o...

FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation.

The increase of available large clinical and experimental datasets has contributed to a substantial amount of important contributions in the area of b...

Integrated Molecular Modeling and Machine Learning for Drug Design.

Modern therapeutic development often involves several stages that are interconnected, and multiple iterations are usually required to bring a new drug...

IoMT based smart healthcare system to control outbreaks of the COVID-19 pandemic.

The COVID-19 pandemic caused millions of infections and deaths globally requiring effective solutions to fight the pandemic. The Internet of Things (I...

Estimation of right lobe graft weight for living donor liver transplantation using deep learning-based fully automatic computed tomographic volumetry.

This study aimed at developing a fully automatic technique for right lobe graft weight estimation using deep learning algorithms. The proposed method ...

Computer-aided diagnosis of chest X-ray for COVID-19 diagnosis in external validation study by radiologists with and without deep learning system.

To evaluate the diagnostic performance of our deep learning (DL) model of COVID-19 and investigate whether the diagnostic performance of radiologists ...

Application of multiple-finding segmentation utilizing Mask R-CNN-based deep learning in a rat model of drug-induced liver injury.

Drug-induced liver injury (DILI) presents significant diagnostic challenges, and recently artificial intelligence-based deep learning technology has b...

Application of deep learning technology for temporal analysis of videofluoroscopic swallowing studies.

Temporal parameters during swallowing are analyzed for objective and quantitative evaluation of videofluoroscopic swallowing studies (VFSS). Manual an...

MalariaSED: a deep learning framework to decipher the regulatory contributions of noncoding variants in malaria parasites.

Malaria remains one of the deadliest infectious diseases. Transcriptional regulation effects of noncoding variants in this unusual genome of malaria p...

Aggregate Formation and Antibody Stability in Infusion Bags: The Impact of Manual and Robotic Compounding of Monoclonal Antibodies.

Monoclonal antibodies (mAbs) can be damaged during the aseptic compounding process, with aggregation being the most prevalent form of degradation. Pro...

Polygenic modelling and machine learning approaches in pharmacogenomics: Importance in downstream analysis of genome-wide association study data.

Genome-wide association studies (GWAS) have identified genetic variations associated with adverse drug effects in pharmacogenomics (PGx) research. How...

Bacterial metabolism-triggered-chemiluminescence-based point-of-care testing platform for sensitive detection and photothermal inactivation of Staphylococcus aureus.

Post-operative pathogenic infections in liver transplantation seriously threaten human health. It is essential to develop novel methods for the highly...