Pneumonia

Machine learning models to identify low adherence to influenza vaccination among Korean adults with cardiovascular disease.

BACKGROUND: Annual influenza vaccination is an important public health measure to prevent influenza infections and is strongly recommended for cardiovascular disease (CVD) patients, especially in the current coronavirus disease 2019 (COVID-19) pandemic. The aim of this study is to develop a machine learning model to identify Korean adult CVD patients with low adherence to influenza vaccination MET...

Towards in silico Process Modeling for Vaccines.

Chemical, manufacturing, and control development timelines occupy a significant part of vaccine end-to-end development. In the on-going race for accelerating timelines, in silico process development constitutes a viable strategy that can be achieved through an artificial intelligence (AI)-driven or a mechanistically oriented approach. In this opinion, we focus on the mechanistic option and report ...

COVID-19 in early 2021: current status and looking forward.

Since the first description of a coronavirus-related pneumonia outbreak in December 2019, the virus SARS-CoV-2 that causes the infection/disease (COVI...

Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneum...

Convolutional neural network model based on radiological images to support COVID-19 diagnosis: Evaluating database biases.

As SARS-CoV-2 has spread quickly throughout the world, the scientific community has spent major efforts on better understanding the characteristics of...

Metaheuristic-based Deep COVID-19 Screening Model from Chest X-Ray Images.

COVID-19 has affected the whole world drastically. A huge number of people have lost their lives due to this pandemic. Early detection of COVID-19 inf...

Accurately Discriminating COVID-19 from Viral and Bacterial Pneumonia According to CT Images Via Deep Learning.

Computed tomography (CT) is one of the most efficient diagnostic methods for rapid diagnosis of the widespread COVID-19. However, reading CT films bri...

On the Juridical Relevance of the Phenomenological Notion of Person in Max Scheler and Edith Stein.

The paper presents a semiotic interpretation of the phenomenological debate on the notion of person, focusing in particular on Edmund Husserl, Max Sch...

Auxiliary Diagnosis for COVID-19 with Deep Transfer Learning.

To assist physicians identify COVID-19 and its manifestations through the automatic COVID-19 recognition and classification in chest CT images with de...

Diagnosis of normal chest radiographs using an autonomous deep-learning algorithm.

AIM: To evaluate the suitability of a deep-learning (DL) algorithm for identifying normality as a rule-out test for fully automated diagnosis in front...

Performance of a deep-learning algorithm for referable thoracic abnormalities on chest radiographs: A multicenter study of a health screening cohort.

PURPOSE: This study evaluated the performance of a commercially available deep-learning algorithm (DLA) (Insight CXR, Lunit, Seoul, South Korea) for r...

Deep Learning for Detection of Elevated Pulmonary Artery Wedge Pressure Using Standard Chest X-Ray.

BACKGROUND: To accurately diagnose and control heart failure (HF), it is important to carry out a simple assessment of elevated pulmonary arterial wed...

Utilizing Computational Machine Learning Tools to Understand Immunogenic Breadth in the Context of a CD8 T-Cell Mediated HIV Response.

Predictive models are becoming more and more commonplace as tools for candidate antigen discovery to meet the challenges of enabling epitope mapping o...

IMI-Bigpicture: A Central Repository for Digital Pathology.

To address the challenges posed by large-scale development, validation, and adoption of artificial intelligence (AI) in pathology, we have constituted...

Unsupervised Deep Anomaly Detection in Chest Radiographs.

The purposes of this study are to propose an unsupervised anomaly detection method based on a deep neural network (DNN) model, which requires only nor...

Deep metric learning-based image retrieval system for chest radiograph and its clinical applications in COVID-19.

In recent years, deep learning-based image analysis methods have been widely applied in computer-aided detection, diagnosis and prognosis, and has sho...

An in silico deep learning approach to multi-epitope vaccine design: a SARS-CoV-2 case study.

The rampant spread of COVID-19, an infectious disease caused by SARS-CoV-2, all over the world has led to over millions of deaths, and devastated the ...

Six artificial intelligence paradigms for tissue characterisation and classification of non-COVID-19 pneumonia against COVID-19 pneumonia in computed tomography lungs.

BACKGROUND: COVID-19 pandemic has currently no vaccines. Thus, the only feasible solution for prevention relies on the detection of COVID-19-positive ...

Hybrid ensemble model for differential diagnosis between COVID-19 and common viral pneumonia by chest X-ray radiograph.

BACKGROUND: Chest X-ray radiography (CXR) has been widely considered as an accessible, feasible, and convenient method to evaluate suspected patients'...

Machine learning applied on chest x-ray can aid in the diagnosis of COVID-19: a first experience from Lombardy, Italy.

BACKGROUND: We aimed to train and test a deep learning classifier to support the diagnosis of coronavirus disease 2019 (COVID-19) using chest x-ray (C...