AIMC Topic: SARS-CoV-2

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An interpretable multi-task system for clinically applicable COVID-19 diagnosis using CXR.

Journal of X-ray science and technology
BACKGROUND: With the emergence of continuously mutating variants of coronavirus, it is urgent to develop a deep learning model for automatic COVID-19 diagnosis at early stages from chest X-ray images. Since laboratory testing is time-consuming and re...

Pre- and Post-publication Verification for Reproducible Data Mining in Macromolecular Crystallography.

Methods in molecular biology (Clifton, N.J.)
Like an article narrative is deemed by an editor and referees to be worthy of being a version of record on acceptance as a publication, so must the underpinning data also be scrutinized before passing it as a version of record. Indeed without the und...

Optimized chest X-ray image semantic segmentation networks for COVID-19 early detection.

Journal of X-ray science and technology
BACKGROUND: Although detection of COVID-19 from chest X-ray radiography (CXR) images is faster than PCR sputum testing, the accuracy of detecting COVID-19 from CXR images is lacking in the existing deep learning models.

AI-driven deep convolutional neural networks for chest X-ray pathology identification.

Journal of X-ray science and technology
BACKGROUND: Chest X-ray images are widely used to detect many different lung diseases. However, reading chest X-ray images to accurately detect and classify different lung diseases by doctors is often difficult with large inter-reader variability. Th...

Digital Healthcare for Airway Diseases from Personal Environmental Exposure.

Yonsei medical journal
Digital technologies have emerged in various dimensions of human life, ranging from education to professional services to well-being. In particular, health products and services have expanded by the use and development of artificial intelligence, mob...

Detecting Racial/Ethnic Health Disparities Using Deep Learning From Frontal Chest Radiography.

Journal of the American College of Radiology : JACR
PURPOSE: The aim of this study was to assess racial/ethnic and socioeconomic disparities in the difference between atherosclerotic vascular disease prevalence measured by a multitask convolutional neural network (CNN) deep learning model using fronta...

Precision Medicine: Using Artificial Intelligence to Improve Diagnostics and Healthcare.

Pacific Symposium on Biocomputing. Pacific Symposium on Biocomputing
The continued generation of large amounts of data within healthcare-from imaging to electronic medical health records to genomics and multi-omics -necessitates tools and methods to parse and interpret these data to improve healthcare outcomes. Artifi...

UBNet: Deep learning-based approach for automatic X-ray image detection of pneumonia and COVID-19 patients.

Journal of X-ray science and technology
BACKGROUND: Analysis of chest X-ray images is one of the primary standards in diagnosing patients with COVID-19 and pneumonia, which is faster than using PCR Swab method. However, accuracy of using X-ray images needs to be improved.

Computer-aided COVID-19 diagnosis and a comparison of deep learners using augmented CXRs.

Journal of X-ray science and technology
BACKGROUND: Coronavirus Disease 2019 (COVID-19) is contagious, producing respiratory tract infection, caused by a newly discovered coronavirus. Its death toll is too high, and early diagnosis is the main problem nowadays. Infected people show a varie...

Fighting COVID-19 with Artificial Intelligence.

Methods in molecular biology (Clifton, N.J.)
The development of vaccines for the treatment of COVID-19 is paving the way for new hope. Despite this, the risk of the virus mutating into a vaccine-resistant variant still persists. As a result, the demand of efficacious drugs to treat COVID-19 is ...