Pneumonia

Assisting the implementation of screening for type 1 diabetes by using artificial intelligence on publicly available data.

The type 1 diabetes community is coalescing around the benefits and advantages of early screening for disease risk. To be accepted by healthcare providers, regulatory authorities and payers, screening programmes need to show that the testing variables allow accurate risk prediction and that individualised risk-informed monitoring plans are established, as well as operational feasibility, cost-effe...

Machine learning models to evaluate mortality in pediatric patients with pneumonia in the intensive care unit.

OBJECTIVES: This study aimed to predict mortality in children with pneumonia who were admitted to the intensive care unit (ICU) to aid decision-making.

A deep-learning-based framework for identifying and localizing multiple abnormalities and assessing cardiomegaly in chest X-ray.

Accurate identification and localization of multiple abnormalities are crucial steps in the interpretation of chest X-rays (CXRs); however, the lack o...

Optimization strategy of community planning for environmental health and public health in smart city under multi-objectives.

As population density increases, environmental hygiene and public health become increasingly severe. As the space where residents stay for the longest...

The communication of artificial intelligence and deep learning in computer tomography image recognition of epidemic pulmonary infectious diseases.

The objectives are to improve the diagnostic efficiency and accuracy of epidemic pulmonary infectious diseases and to study the application of artific...

Validating the accuracy of deep learning for the diagnosis of pneumonia on chest x-ray against a robust multimodal reference diagnosis: a post hoc analysis of two prospective studies.

BACKGROUND: Artificial intelligence (AI) seems promising in diagnosing pneumonia on chest x-rays (CXR), but deep learning (DL) algorithms have primari...

Lung-DT: An AI-Powered Digital Twin Framework for Thoracic Health Monitoring and Diagnosis.

The integration of artificial intelligence (AI) with Digital Twins (DTs) has emerged as a promising approach to revolutionize healthcare, particularly...

ChatGPT and Corporations of Mega-journals Jeopardize the Norms That Underpin Academic Publishing.

Those who participate in and contribute to academic publishing are affected by its evolution. Funding bodies, academic institutions, researchers and p...

Fall risk classification with posturographic parameters in community-dwelling older adults: a machine learning and explainable artificial intelligence approach.

BACKGROUND: Computerized posturography obtained in standing conditions has been applied to classify fall risk for older adults or disease groups. Comb...

Diagnostic performance of deep learning models versus radiologists in COVID-19 pneumonia: A systematic review and meta-analysis.

PURPOSE: Although several studies have compared the performance of deep learning (DL) models and radiologists for the diagnosis of COVID-19 pneumonia ...

Searching for information about stem cells online in an age of artificial intelligence: How should the stem cell community respond?

Patients and their families routinely use the Internet to learn about stem cell research. What they find, is increasingly influenced by ongoing change...

Toward the eradication of medical diagnostic errors.

The medical community does not broadcast the problem, but there are many studies that have reinforced a serious issue with diagnostic errors. A recent...

Opportunities and challenges of artificial intelligence and distributed systems to improve the quality of healthcare service.

The healthcare sector, characterized by vast datasets and many diseases, is pivotal in shaping community health and overall quality of life. Tradition...

Pneumonia detection based on RSNA dataset and anchor-free deep learning detector.

Pneumonia is a highly lethal disease, and research on its treatment and early screening tools has received extensive attention from researchers. Due t...

Explainable deep learning diagnostic system for prediction of lung disease from medical images.

Around the globe, respiratory lung diseases pose a severe threat to human survival. Based on a central goal to reduce contiguous transmission from inf...

MI-DenseCFNet: deep learning-based multimodal diagnosis models for Aureus and Aspergillus pneumonia.

OBJECTIVE: To build and merge a diagnostic model called multi-input DenseNet fused with clinical features (MI-DenseCFNet) for discriminating between S...

Scoping Review of Deep Learning Techniques for Diagnosis, Drug Discovery, and Vaccine Development in Leishmaniasis.

, a single-cell parasite prevalent in tropical and subtropical regions worldwide, can cause varying degrees of leishmaniasis, ranging from self-limiti...

Generative artificial intelligence can have a role in combating vaccine hesitancy.

Artificial intelligence has potential to counter vaccine hesitancy while building trust in vaccines, but it must be deployed ethically and responsibly...

Comparison of hepatitis B and SARS-CoV2 vaccination rates in people who attended Drugs and Addiction Centres.

BACKGROUND AND AIMS: Persons with substance use disorder are at increased risk for hepatitis B virus (HBV) infection. Although most of them are attach...

Artificial Intelligence-enabled Chest X-ray Classifies Osteoporosis and Identifies Mortality Risk.

A deep learning model was developed to identify osteoporosis from chest X-ray (CXR) features with high accuracy in internal and external validation. I...