Cardiovascular

High-throughput DeepPRM-Stellar proteomics coupled with machine learning enables precise quantification of atherosclerosis-stroke progression biomarkers and risk prediction.

Predicting the progression of asymptomatic large-artery atherosclerosis (LAA) to acute ischemic stro...

Natural Language Processing framework for identifying abdominal aortic aneurysm repairs using unstructured electronic health records.

Patient identification for national registries often relies upon clinician recognition of cases or r...

Automatic measuring of coronary atherosclerosis from medicolegal autopsy photographs based on deep learning techniques.

A diagnosis of atherosclerotic cardiovascular disease is critical importance in forensic medicine, p...

Identification of clinical diagnostic and immune cell infiltration characteristics of acute myocardial infarction with machine learning approach.

Acute myocardial infarction (AMI) is a serious heart disease with high fatality rates. The progress ...

Artificial intelligence in coronary angiography: benchmarking the diagnostic accuracy of ChatGPT-4o against interventional cardiologists.

BACKGROUND: The integration of artificial intelligence (AI) into medical diagnostics has significant...

Predicting arterial pressure without prejudice: towards effective hypotension prediction models.

Selection bias has been identified in hypotension prediction models, but its impact on an algorithm'...

Generative artificial intelligence in cardiovascular specialty care: a scoping review.

BACKGROUND: The current research on the application of generative artificial intelligence in has sho...

Enhancing cardiac disease detection via a fusion of machine learning and medical imaging.

Cardiovascular illnesses continue to be a predominant cause of mortality globally, underscoring the ...

Deep learning to identify stroke within 4.5 h using DWI and FLAIR in a prospective multicenter study.

To enhance thrombolysis eligibility in acute ischemic stroke, we developed a deep learning model to ...

ISENet: a deep learning model for detecting ischemic ST changes in long-term ECG monitoring.

Long-term ECG monitoring is crucial for detecting asymptomatic or intermittent myocardial ischemia, ...

Latent Class Analysis Identifies Distinct Patient Phenotypes Associated With Mistaken Treatment Decisions and Adverse Outcomes in Coronary Artery Disease.

This study aimed to identify patient characteristics linked to mistaken treatments and major adverse...

An Artificial Intelligence and Machine Learning model for Personalized Prediction of Long-term Mitral Valve Repair Durability.

OBJECTIVES: To compare Random Survival Forest (RSF), a machine learning (ML) method, with Cox Propor...

`Probabilistic Ensemble Learning for Prediction of Stroke Thrombectomy Outcomes from the NeuroVascular Quality Initiative - Quality Outcomes Database (NVQI-QOD) Registry.

INTRODUCTION: Mechanical Thrombectomy (MT) is the standard of care in the interventional management ...

CT derived fractional flow reserve: Part 1 - Comprehensive review of methodologies.

Advancements in cardiac computed tomography angiography (CCTA) have enabled the extraction of physio...

Semantic web-based ontology: a comprehensive framework for cardiovascular knowledge representation.

In the healthcare industry, the Semantic Web offers to manage a huge amount of medical data which is...

Analyses of the mechanism and therapeutic targets of senescence related genes in ischemic stroke with multi-omics approach.

Ischemic stroke (IS) affects 11 million people annually, posing substantial clinical and economic bu...

A comprehensive explainable AI approach for enhancing transparency and interpretability in stroke prediction.

Stroke is among the leading causes of death, especially among old adults. Thus, the mortality rate a...

A multimodal dataset for training deep learning models aimed at detecting and analyzing sleep apnea.

Sleep Apnea Syndrome (SAS) is a serious respiratory disorder that can lead to a range of complicatio...

Comparing the Performance of Machine Learning Models and Conventional Risk Scores for Predicting Major Adverse Cardiovascular Cerebrovascular Events After Percutaneous Coronary Intervention in Patients With Acute Myocardial Infarction: Systematic Review and Meta-Analysis.

BACKGROUND: Machine learning (ML) models may offer greater clinical utility than conventional risk s...

Letter regarding the article 'Employment of artificial intelligence for an unbiased evaluation regarding the recovery of right ventricular function after mitral valve transcatheter edge-to-edge repair'.

Reply to the letter regarding the article 'Employment of artificial intelligence for an unbiased evaluation regarding the recovery of right ventricular function after mitral valve transcatheter edge-to-edge repair'.