COVID-19

Simultaneous phenotyping of five Rh red blood cell antigens on a paper-based analytical device combined with deep learning for rapid and accurate interpretation.

Both the ABO and Rhesus (Rh) blood groups play crucial roles in blood transfusion medicine. Herein, we report a simple and low-cost paper-based analytical device (PAD) for phenotyping red blood cell (RBC) antigens. Using this Rh typing format, 5 Rh antigens on RBCs can be simultaneously detected and macroscopically visualized within 12 min. The proposed Rh phenotyping relies on the presence or abs...

A Real-Time Crowd Monitoring and Management System for Social Distance Classification and Healthcare Using Deep Learning.

Coronavirus born COVID-19 disease has spread its roots in the whole world. It is primarily spread by physical contact. As a preventive measure, proper crowd monitoring and management systems are required to be installed in public places to limit sudden outbreaks and impart improved healthcare. The number of new infections can be significantly reduced by adopting social distancing measures earlier....

An Interpretable Chest CT Deep Learning Algorithm for Quantification of COVID-19 Lung Disease and Prediction of Inpatient Morbidity and Mortality.

RATIONALE AND OBJECTIVES: The burden of coronavirus disease 2019 (COVID-19) airspace opacities is time consuming and challenging to quantify on comput...

Think positive: An interpretable neural network for image recognition.

The COVID-19 pandemic is an ongoing pandemic and is placing additional burden on healthcare systems around the world. Timely and effectively detecting...

Tracking and predicting COVID-19 radiological trajectory on chest X-rays using deep learning.

Radiological findings on chest X-ray (CXR) have shown to be essential for the proper management of COVID-19 patients as the maximum severity over the ...

Subtraction Gates: Another Way to Learn Long-Term Dependencies in Recurrent Neural Networks.

Recurrent neural networks (RNNs) can remember temporal contextual information over various time steps. The well-known gradient vanishing/explosion pro...

Deep learning-based anomaly detection from ultrasonic images.

Non-destructive testing is a group of methods for evaluating the integrity of components. Among them, ultrasonic inspection stands out due to its abil...

A new dynamic deep learning noise elimination method for chip-based real-time PCR.

Point-of-care (POC) real-time polymerase chain reaction (PCR) has become one of the most important technologies for many fields such as pathogen detec...

State-of-the-Art Capability of Convolutional Neural Networks to Distinguish the Signal in the Ionosphere.

Recovering and distinguishing different ionospheric layers and signals usually requires slow and complicated procedures. In this work, we construct an...

Generalizability assessment of COVID-19 3D CT data for deep learning-based disease detection.

BACKGROUND: Artificial intelligence technologies in classification/detection of COVID-19 positive cases suffer from generalizability. Moreover, access...

Evaluation of the humoral response induced by BBIBP-CorV vaccine by determining neutralizing antibodies in peruvian healthcare personnel.

OBJECTIVE.: To determine the titer of antibodies against the receptor binding domain (RBD) of the spike protein (S) in health personnel between the 4t...

Transferability of multivariate extreme value models for safety assessment by applying artificial intelligence-based video analytics.

Traffic conflict techniques represent the state-of-the-art for road safety assessments. However, the lack of research on transferability of conflict-b...

DeBoNet: A deep bone suppression model ensemble to improve disease detection in chest radiographs.

Automatic detection of some pulmonary abnormalities using chest X-rays may be impacted adversely due to obscuring by bony structures like the ribs and...

Engineering nonlinear epileptic biomarkers using deep learning and Benford's law.

In this study, we designed two deep neural networks to encode 16 features for early seizure detection in intracranial EEG and compared them and their ...

Machine Learning with Quantum Seagull Optimization Model for COVID-19 Chest X-Ray Image Classification.

Early and accurate detection of COVID-19 is an essential process to curb the spread of this deadly disease and its mortality rate. Chest radiology sca...

Automated System for Identifying COVID-19 Infections in Computed Tomography Images Using Deep Learning Models.

Coronavirus disease 2019 (COVID-19) is a novel disease that affects healthcare on a global scale and cannot be ignored because of its high fatality ra...

An Efficient and Effective Deep Learning-Based Model for Real-Time Face Mask Detection.

Since December 2019, the COVID-19 pandemic has led to a dramatic loss of human lives and caused severe economic crises worldwide. COVID-19 virus trans...

Deep Learning Based SWIR Object Detection in Long-Range Surveillance Systems: An Automated Cross-Spectral Approach.

SWIR imaging bears considerable advantages over visible-light (color) and thermal images in certain challenging propagation conditions. Thus, the SWIR...

Dose prediction via distance-guided deep learning: Initial development for nasopharyngeal carcinoma radiotherapy.

BACKGROUND AND PURPOSE: Geometric information such as distance information is essential for dose calculations in radiotherapy. However, state-of-the-a...

Prediction-error neurons in circuits with multiple neuron types: Formation, refinement, and functional implications.

SignificanceAn influential idea in neuroscience is that neural circuits do not only passively process sensory information but rather actively compare ...