Cardiovascular

Elucidating the role of KCTD10 in coronary atherosclerosis: Harnessing bioinformatics and machine learning to advance understanding.

Atherosclerosis (AS) is increasingly recognized as a chronic inflammatory disease that significantly...

Intelligent risk stratification of hypertension based on ambulatory blood pressure monitoring and machine learning algorithms.

. Risk stratification of hypertension plays a crucial role in the treatment decisions and medication...

Deep learning-based prediction of atrial fibrillation from polar transformed time-frequency electrocardiogram.

Portable and wearable electrocardiogram (ECG) devices are increasingly utilized in healthcare for mo...

Combating cardiovascular disease disparities: The potential role of artificial intelligence.

Development and validation of an interpretable machine learning model for predicting in-hospital mortality for ischemic stroke patients in ICU.

BACKGROUND: Timely and accurate outcome prediction is essential for clinical decision-making for isc...

Addressing underestimation and explanation of retinal fundus photo-based cardiovascular disease risk score: Algorithm development and validation.

OBJECTIVE: To resolve the underestimation problem and investigate the mechanism of the AI model whic...

Artificial intelligence in stroke rehabilitation: From acute care to long-term recovery.

Stroke is a leading cause of disability worldwide, driving the need for advanced rehabilitation stra...

Predictive value of machine learning for in-hospital mortality risk in acute myocardial infarction: A systematic review and meta-analysis.

BACKGROUND: Machine learning (ML) models have been constructed to predict the risk of in-hospital mo...

Rapid Blood Clot Removal via Remote Delamination and Magnetization of Clot Debris.

Micro/nano-scale robotic devices are emerging as a cutting-edge approach for precision intravascular...

Integrated fusion approach for multi-class heart disease classification through ECG and PCG signals with deep hybrid neural networks.

Detection and classification of cardiovascular diseases are crucial for early diagnosis and predicti...

Role of oxidative balance score in staging and mortality risk of cardiovascular-kidney-metabolic syndrome: Insights from traditional and machine learning approaches.

OBJECTIVES: To evaluate the roles of oxidative balance score (OBS) in staging and mortality risk of ...

Rehabilitation training robot using mirror therapy for the upper and lower limb after stroke: a prospective cohort study.

BACKGROUND: This prospective cohort study was designed to investigate and compare the effectiveness ...

Diagnosis extraction from unstructured Dutch echocardiogram reports using span- and document-level characteristic classification.

BACKGROUND: Clinical machine learning research and artificial intelligence driven clinical decision ...

Comment on "Forecasting intraoperative hypotension during hepatobiliary surgery".

Dynamic Prediction and Intervention of Serum Sodium in Patients with Stroke Based on Attention Mechanism Model.

Abnormal serum sodium levels are a common and severe complication in stroke patients, significantly ...

AI integrations with lung cancer screening: Considerations in developing AI in a public health setting.

Lung cancer screening implementation has led to expanded imaging of the chest in older, tobacco-expo...

Applications of Artificial Intelligence in Drug Repurposing.

Drug repurposing identifies new therapeutic uses for the existing drugs originally developed for dif...

How Can Robotic Devices Help Clinicians Determine the Treatment Dose for Post-Stroke Arm Paresis?

Upper limb training dose after stroke is usually quantified by time and repetitions. This study anal...

Machine Learning Models With Prognostic Implications for Predicting Gastrointestinal Bleeding After Coronary Artery Bypass Grafting and Guiding Personalized Medicine: Multicenter Cohort Study.

BACKGROUND: Gastrointestinal bleeding is a serious adverse event of coronary artery bypass grafting ...

A novel hybrid CNN-transformer model for arrhythmia detection without R-peak identification using stockwell transform.

This study presents a novel hybrid deep learning model for arrhythmia classification from electrocar...

Automatic detecting multiple bone metastases in breast cancer using deep learning based on low-resolution bone scan images.

Whole-body bone scan (WBS) is usually used as the effective diagnostic method for early-stage and co...