AI Medical Compendium Topic

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Cardiovascular Diseases

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Artificial intelligence-enhanced retinal imaging as a biomarker for systemic diseases.

Theranostics
Retinal images provide a non-invasive and accessible means to directly visualize human blood vessels and nerve fibers. Growing studies have investigated the intricate microvascular and neural circuitry within the retina, its interactions with other s...

A cross-sectional study comparing machine learning and logistic regression techniques for predicting osteoporosis in a group at high risk of cardiovascular disease among old adults.

BMC geriatrics
BACKGROUND: Osteoporosis has become a significant public health concern that necessitates the application of appropriate techniques to calculate disease risk. Traditional methods, such as logistic regression,have been widely used to identify risk fac...

Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.

Scientific reports
Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challen...

Catenation between mHealth application advertisements and cardiovascular diseases: moderation of artificial intelligence (AI)-enabled internet of things, digital divide, and individual trust.

BMC public health
BACKGROUND: World Health Organization (WHO) identified noncommunicable diseases as the foremost risk to public health globally and the cause of approximately 80% of premature deaths. However, Cardiovascular diseases account for most of these prematur...

Machine learning reveals distinct neuroanatomical signatures of cardiovascular and metabolic diseases in cognitively unimpaired individuals.

Nature communications
Comorbid cardiovascular and metabolic risk factors (CVM) differentially impact brain structure and increase dementia risk, but their specific magnetic resonance imaging signatures (MRI) remain poorly characterized. To address this, we developed and v...

Life's Crucial 9 and NAFLD from association to SHAP-interpreted machine learning predictions.

Scientific reports
Non-alcoholic fatty liver disease (NAFLD) is the most prevalent chronic liver disease worldwide. Cardiovascular disease (CVD) and NAFLD share multiple common risk factors. Life's Crucial 9 (LC9), a novel indicator for comprehensive assessment of card...

Research on Prediction model of Carotid-Femoral Pulse Wave Velocity: Based on Machine Learning Algorithm.

Journal of clinical hypertension (Greenwich, Conn.)
Carotid-femoral pulse wave velocity (cf-PWV) is an important but difficult to obtain measure of arterial stiffness and an independent predictor of cardiovascular events and all-cause mortality. The objective of this study was to develop a predictive ...

A domain adaptation model for carotid ultrasound: Image harmonization, noise reduction, and impact on cardiovascular risk markers.

Computers in biology and medicine
Deep learning has been used extensively for medical image analysis applications, assuming the training and test data adhere to the same probability distributions. However, a common challenge arises when dealing with medical images generated by differ...

Machine learning and multi-omics integration: advancing cardiovascular translational research and clinical practice.

Journal of translational medicine
The global burden of cardiovascular diseases continues to rise, making their prevention, diagnosis and treatment increasingly critical. With advancements and breakthroughs in omics technologies such as high-throughput sequencing, multi-omics approach...

Improved prediction and risk stratification of major adverse cardiovascular events using an explainable machine learning approach combining plasma biomarkers and traditional risk factors.

Cardiovascular diabetology
BACKGROUND: Cardiovascular diseases (CVD) remain the leading cause of morbidity and mortality globally. Traditional risk models, primarily based on established risk factors, often lack the precision needed to accurately predict new-onset major advers...