Pneumonia

Discriminative Feature Learning for Thorax Disease Classification in Chest X-ray Images.

This paper focuses on the thorax disease classification problem in chest X-ray (CXR) images. Different from the generic image classification task, a robust and stable CXR image analysis system should consider the unique characteristics of CXR images. Particularly, it should be able to: 1) automatically focus on the disease-critical regions, which usually are of small sizes; 2) adaptively capture t...

Machine learning approaches reveal subtle differences in breathing and sleep fragmentation in -derived astrocytes ablated mice.

Modern neurophysiology research requires the interrogation of high-dimensionality data sets. Machine learning and artificial intelligence (ML/AI) workflows have permeated into nearly all aspects of daily life in the developed world but have not been implemented routinely in neurophysiological analyses. The power of these workflows includes the speed at which they can be deployed, their availabilit...

Machine and Deep Learning towards COVID-19 Diagnosis and Treatment: Survey, Challenges, and Future Directions.

With many successful stories, machine learning (ML) and deep learning (DL) have been widely used in our everyday lives in a number of ways. They have ...

How does the radiology community discuss the benefits and limitations of artificial intelligence for their work? A systematic discourse analysis.

PURPOSE: We aimed to systematically analyse how the radiology community discusses the concept of artificial intelligence (AI), perceives its benefits,...

AI Accelerated Human-in-the-loop Structuring of Radiology Reports.

Rule-based Natural Language Processing (NLP) pipelines depend on robust domain knowledge. Given the long tail of important terminology in radiology re...

Association of AI quantified COVID-19 chest CT and patient outcome.

PURPOSE: Severity scoring is a key step in managing patients with COVID-19 pneumonia. However, manual quantitative analysis by radiologists is a time-...

Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods.

BACKGROUND AND OBJECTIVE: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus,...

Artificial Intelligence-assisted chest X-ray assessment scheme for COVID-19.

OBJECTIVES: To study whether a trained convolutional neural network (CNN) can be of assistance to radiologists in differentiating Coronavirus disease ...

Multi-Radiologist User Study for Artificial Intelligence-Guided Grading of COVID-19 Lung Disease Severity on Chest Radiographs.

RATIONALE AND OBJECTIVES: Radiographic findings of COVID-19 pneumonia can be used for patient risk stratification; however, radiologist reporting of d...

A narrative review on characterization of acute respiratory distress syndrome in COVID-19-infected lungs using artificial intelligence.

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 C...

Artificial intelligence matches subjective severity assessment of pneumonia for prediction of patient outcome and need for mechanical ventilation: a cohort study.

To compare the performance of artificial intelligence (AI) and Radiographic Assessment of Lung Edema (RALE) scores from frontal chest radiographs (CXR...

Chest X-ray image phase features for improved diagnosis of COVID-19 using convolutional neural network.

PURPOSE: Recently, the outbreak of the novel coronavirus disease 2019 (COVID-19) pandemic has seriously endangered human health and life. In fighting ...

A new resource on artificial intelligence powered computer automated detection software products for tuberculosis programmes and implementers.

Recently, the number of artificial intelligence powered computer-aided detection (CAD) products that detect tuberculosis (TB)-related abnormalities fr...

Artificial Intelligence Model of Drive-Through Vaccination Simulation.

Planning for mass vaccination against SARS-Cov-2 is ongoing in many countries considering that vaccine will be available for the general public in the...

The usage of deep neural network improves distinguishing COVID-19 from other suspected viral pneumonia by clinicians on chest CT: a real-world study.

OBJECTIVES: Based on the current clinical routine, we aimed to develop a novel deep learning model to distinguish coronavirus disease 2019 (COVID-19) ...

Design of novel orotransmucosal vaccine-delivery platforms using artificial intelligence.

The linings of the oral cavity are excellent needle-free vaccination sites, able to induce immune responses at distal sites and confer systemic protec...

A community effort for automatic detection of postictal generalized EEG suppression in epilepsy.

Applying machine learning to healthcare sheds light on evidence-based decision making and has shown promises to improve healthcare by combining clinic...

Artificial intelligence predicts the immunogenic landscape of SARS-CoV-2 leading to universal blueprints for vaccine designs.

The global population is at present suffering from a pandemic of Coronavirus disease 2019 (COVID-19), caused by the novel coronavirus Severe Acute Res...

Lightweight deep learning models for detecting COVID-19 from chest X-ray images.

Deep learning methods have already enjoyed an unprecedented success in medical imaging problems. Similar success has been evidenced when it comes to t...

Segmentation of COVID-19 pneumonia lesions: A deep learning approach.

Lung CT scan has a pivotal role in diagnosis and monitoring of COVID-19 patients, and with growing number of affected individuals, the need for artif...