AI Medical Compendium Topic:
SARS-CoV-2

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Automated Processing and Phenotype Extraction of Ovine Medical Images Using a Combined Generative Adversarial Network and Computer Vision Pipeline.

Sensors (Basel, Switzerland)
The speed and accuracy of phenotype detection from medical images are some of the most important qualities needed for any informed and timely response such as early detection of cancer or detection of desirable phenotypes for animal breeding. To impr...

Role of Digital Health During Coronavirus Disease 2019 Pandemic and Future Perspectives.

Cardiac electrophysiology clinics
Coronavirus disease 2019 revolutionized the digital health care. This pandemic was the catalyst for not only a sudden but also widespread paradigm shift in patient care, with nearly 80% of the US population indicating that they have used one form of ...

PSCNN: PatchShuffle Convolutional Neural Network for COVID-19 Explainable Diagnosis.

Frontiers in public health
COVID-19 is a sort of infectious disease caused by a new strain of coronavirus. This study aims to develop a more accurate COVID-19 diagnosis system. First, the -conv module (nCM) is introduced. Then we built a 12-layer convolutional neural network...

Impact of Lung Segmentation on the Diagnosis and Explanation of COVID-19 in Chest X-ray Images.

Sensors (Basel, Switzerland)
COVID-19 frequently provokes pneumonia, which can be diagnosed using imaging exams. Chest X-ray (CXR) is often useful because it is cheap, fast, widespread, and uses less radiation. Here, we demonstrate the impact of lung segmentation in COVID-19 ide...

A machine-learning parsimonious multivariable predictive model of mortality risk in patients with Covid-19.

Scientific reports
The COVID-19 pandemic is impressively challenging the healthcare system. Several prognostic models have been validated but few of them are implemented in daily practice. The objective of the study was to validate a machine-learning risk prediction mo...

Susceptible-Infected-Removed Mathematical Model under Deep Learning in Hospital Infection Control of Novel Coronavirus Pneumonia.

Journal of healthcare engineering
OBJECTIVE: This research aimed to explore the application of a mathematical model based on deep learning in hospital infection control of novel coronavirus (COVID-19) pneumonia.

Quantification of pulmonary involvement in COVID-19 pneumonia by means of a cascade of two U-nets: training and assessment on multiple datasets using different annotation criteria.

International journal of computer assisted radiology and surgery
PURPOSE: This study aims at exploiting artificial intelligence (AI) for the identification, segmentation and quantification of COVID-19 pulmonary lesions. The limited data availability and the annotation quality are relevant factors in training AI-me...

A collaborative robotic solution to partly automate SARS-CoV-2 serological tests in small facilities.

SLAS technology
The outbreak of COVID-19 has introduced a significant stress on the healthcare systems of many countries. The availability of quick and reliable screening methodologies can be regarded as the keystone approach to mitigate the spread of the infection ...

COVID-19 detection using chest X-ray images based on a developed deep neural network.

SLAS technology
AIM: Currently, a new coronavirus called COVID-19 is the biggest challenge of the human at 21st century. Now, the spread of this virus is such that mortality has risen strongly in all cities of countries. Therefore, it is necessary to think of a solu...

De novo design of novel protease inhibitor candidates in the treatment of SARS-CoV-2 using deep learning, docking, and molecular dynamic simulations.

Computers in biology and medicine
The main protease of SARS-CoV-2 is a critical target for the design and development of antiviral drugs. 2.5 M compounds were used in this study to train an LSTM generative network via transfer learning in order to identify the four best candidates ca...