AIMC Topic: Humans

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Prediction of new-onset atrial fibrillation in patients with hypertrophic cardiomyopathy using machine learning.

European journal of heart failure
AIMS: Atrial fibrillation (AF) is the most common sustained arrhythmia among patients with hypertrophic cardiomyopathy (HCM), leading to increased symptom burden and risk of thromboembolism. The HCM-AF score was developed to predict new-onset AF in p...

Predicting cyclins based on key features and machine learning methods.

Methods (San Diego, Calif.)
Cyclins are a group of proteins that regulate the cell cycle process by modulating various stages of cell division to ensure correct cell proliferation, differentiation, and apoptosis. Research on cyclins is crucial for understanding the biological f...

Quantitative analysis of deep learning reconstruction in CT angiography: Enhancing CNR and reducing dose.

Journal of X-ray science and technology
BACKGROUND: Computed tomography angiography (CTA) provides significant information on image quality in vascular imaging, thus offering high-resolution images despite having the disadvantages of increased radiation doses and contrast agent-related sid...

Radiomics and deep learning features of pericoronary adipose tissue on non-contrast computerized tomography for predicting non-calcified plaques.

Journal of X-ray science and technology
BACKGROUND: Inflammation of coronary arterial plaque is considered a key factor in the development of coronary heart disease. Early the plaque detection and timely treatment of the atherosclerosis could effectively reduce the risk of cardiovascular e...

[Impact of artificial intelligence on the evolution of clinical practices in oncology: Focus on language models].

Bulletin du cancer
Artificial intelligence (AI) is addressing many expectations for healthcare practitioners and patients in oncology. It has the potential to deeply transform medical practices as we know them today: improving early diagnosis by analysing large quantit...

Spatiotemporal modeling of long-term PM concentrations and population exposure in Greece, using machine learning and statistical methods.

The Science of the total environment
The lack of high-resolution, long-term PM observations in Greece and the Eastern Mediterranean hampers the development of spatial models that are crucial for providing representative exposure estimates to health studies. This work presents a spatial ...

Recognizing SARS-CoV-2 infection of nasopharyngeal tissue at the single-cell level by machine learning method.

Molecular immunology
SARS-CoV-2 has posed serious global health challenges not only because of the high degree of virus transmissibility but also due to its severe effects on the respiratory system, such as inducing changes in multiple organs through the ACE2 receptor. T...

Knowledge is not all you need for comfort in use of AI in healthcare.

Public health
OBJECTIVES: The adoption of artificial intelligence (AI) in healthcare is rapidly expanding, transforming areas such as diagnostics, drug discovery, and patient monitoring. Despite these advances, public perceptions of AI in healthcare, particularly ...

Determining structures of RNA conformers using AFM and deep neural networks.

Nature
Much of the human genome is transcribed into RNAs, many of which contain structural elements that are important for their function. Such RNA molecules-including those that are structured and well-folded-are conformationally heterogeneous and flexible...

Report of the First ONTOX Hackathon: Hack to Save Lives and Avoid Animal Suffering. The Use of Artificial Intelligence in Toxicology - A Potential Driver for Reducing/Replacing Laboratory Animals in the Future.

Alternatives to laboratory animals : ATLA
The first ONTOX Hackathon of the EU Horizon 2020-funded ONTOX project was held on 21-23 April 2024 in Utrecht, The Netherlands (https://ontox-project.eu/hackathon/). This participatory event aimed to collectively advance innovation for human safety t...