AIMC Topic:
SARS-CoV-2
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Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.
Academic radiology
Jan 18, 2021
RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of disease severity is inconsistent on chest radiographs (CXRs). We aimed to see if an artificial intell...
A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.
Computers in biology and medicine
Jan 18, 2021
COVID-19 has infected 77.4 million people worldwide and has caused 1.7 million fatalities as of December 21, 2020. The primary cause of death due to COVID-19 is Acute Respiratory Distress Syndrome (ARDS). According to the World Health Organization (W...
Use of machine learning to identify a T cell response to SARS-CoV-2.
Cell reports. Medicine
Jan 16, 2021
The identification of SARS-CoV-2-specific T cell receptor (TCR) sequences is critical for understanding T cell responses to SARS-CoV-2. Accordingly, we reanalyze publicly available data from SARS-CoV-2-recovered patients who had low-severity disease ...
Machine learning in a time of COVID-19 - Can machine learning support Community Health Workers (CHWs) in low and middle income countries (LMICs) in the new normal?
Journal of global health
Jan 16, 2021
CT image segmentation for inflamed and fibrotic lungs using a multi-resolution convolutional neural network.
Scientific reports
Jan 14, 2021
The purpose of this study was to develop a fully-automated segmentation algorithm, robust to various density enhancing lung abnormalities, to facilitate rapid quantitative analysis of computed tomography images. A polymorphic training approach is pro...
Classification and specific primer design for accurate detection of SARS-CoV-2 using deep learning.
Scientific reports
Jan 13, 2021
In this paper, deep learning is coupled with explainable artificial intelligence techniques for the discovery of representative genomic sequences in SARS-CoV-2. A convolutional neural network classifier is first trained on 553 sequences from the Nati...
A Novel Protein Mapping Method for Predicting the Protein Interactions in COVID-19 Disease by Deep Learning.
Interdisciplinary sciences, computational life sciences
Jan 12, 2021
The new type of corona virus (SARS-COV-2) emerging in Wuhan, China has spread rapidly to the world and has become a pandemic. In addition to having a significant impact on daily life, it also shows its effect in different areas, including public heal...
Explainable COVID-19 Detection Using Chest CT Scans and Deep Learning.
Sensors (Basel, Switzerland)
Jan 11, 2021
This paper explores how well deep learning models trained on chest CT images can diagnose COVID-19 infected people in a fast and automated process. To this end, we adopted advanced deep network architectures and proposed a transfer learning strategy ...
A novel deep learning-based quantification of serial chest computed tomography in Coronavirus Disease 2019 (COVID-19).
Scientific reports
Jan 11, 2021
This study aims to explore and compare a novel deep learning-based quantification with the conventional semi-quantitative computed tomography (CT) scoring for the serial chest CT scans of COVID-19. 95 patients with confirmed COVID-19 and a total of 4...
Accurately Differentiating Between Patients With COVID-19, Patients With Other Viral Infections, and Healthy Individuals: Multimodal Late Fusion Learning Approach.
Journal of medical Internet research
Jan 6, 2021
BACKGROUND: Effectively identifying patients with COVID-19 using nonpolymerase chain reaction biomedical data is critical for achieving optimal clinical outcomes. Currently, there is a lack of comprehensive understanding in various biomedical feature...
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