Cardiovascular

A novel intelligent grade classification architecture for Patent Foramen Ovale by Contrast Transthoracic Echocardiography based on deep learning.

Patent foramen ovale (PFO) is one of the main causes of ischemic stroke. Due to the complex characte...

Shaping the Future of Cardiac Anesthesia: Emerging Trends and Research Directions.

This article provides an overview of knowledge gaps that need to be addressed in perioperative cardi...

Assessment of the long RR intervals using convolutional neural networks in single-lead long-term Holter electrocardiogram recordings.

Advancements in medical technology have extended long-term electrocardiogram (ECG) monitoring from t...

ECG-based heart arrhythmia classification using feature engineering and a hybrid stacked machine learning.

A heart arrhythmia refers to a set of conditions characterized by irregular heart- beats, with an in...

Interim results of exoskeletal wearable robot for gait recovery in subacute stroke patients.

Exoskeletons have been proposed for potential clinical use to improve ambulatory function in patient...

Establishing a clinical prediction model for diabetic foot ulcers in type 2 diabetic patients with lower extremity arteriosclerotic occlusion using machine learning.

The burden of diabetic foot ulcers (DFU) is exacerbated in diabetic patients with concomitant arteri...

Predator crow search optimization with explainable AI for cardiac vascular disease classification.

The proposed framework optimizes Explainable AI parameters, combining Predator crow search optimizat...

Coronary p-Graph: Automatic classification and localization of coronary artery stenosis from Cardiac CTA using DSA-based annotations.

Coronary artery disease (CAD) is a prevalent cardiovascular condition with profound health implicati...

Implementation of a national AI technology program on cardiovascular outcomes and the health system.

Coronary artery disease (CAD) is a major cause of ill health and death worldwide. Coronary computed ...

An effective PO-RSNN and FZCIS based diabetes prediction and stroke analysis in the metaverse environment.

Chronic disease (CD) like diabetes and stroke impacts global healthcare extensively, and continuous ...

Responsible CVD screening with a blockchain assisted chatbot powered by explainable AI.

Cardiovascular disease (CVD) is rising as a significant concern for the healthcare sector around the...

Unsupervised Domain Adaptation for Cross-Modality Cerebrovascular Segmentation.

Cerebrovascular segmentation from time-of-flight magnetic resonance angiography (TOF-MRA) and comput...

Unpaired Optical Coherence Tomography Angiography Image Super-Resolution via Frequency-Aware Inverse-Consistency GAN.

For optical coherence tomography angiography (OCTA) images, the limited scanning rate leads to a tra...

Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.

BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (...

A machine learning model to predict intradialytic hypotension in pediatric continuous kidney replacement therapy.

BACKGROUND: Intradialytic hypotension (IDH) is associated with mortality in adults undergoing interm...

Can small dense LDL cholesterol be estimated from the lipid profile?

PURPOSE OF REVIEW: Small dense low-density lipoprotein cholesterol (sdLDL-C) is recognized for its s...

Comment on "Machine learning risk-prediction model for in-hospital mortality in Takotsubo cardiomyopathy".

Predicting the risk of ischemic stroke in patients with atrial fibrillation using heterogeneous drug-protein-disease network-based deep learning.

Current risk assessment models for predicting ischemic stroke (IS) in patients with atrial fibrillat...

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

Deep learning has been used extensively for medical image analysis applications, assuming the traini...

An enhanced CNN-Bi-transformer based framework for detection of neurological illnesses through neurocardiac data fusion.

Classical approaches to diagnosis frequently rely on self-reported symptoms or clinician observation...

Explainable machine learning model based on EEG, ECG, and clinical features for predicting neurological outcomes in cardiac arrest patient.

Early and accurate prediction of neurological outcomes in comatose patients following cardiac arrest...