Validation of a Prediction Model From Quantitative Coronary Angiography to Detect Ischaemic Lesions as Evaluated by Invasive Fractional Flow Reserve.

Aim: Physician visual assessment (PVA) in invasive coronary angiography (ICA) is clinically used to determine stenosis severity and guide coronary intervention. However, PVA provides limited information regarding the haemodynamic significance of stenosis. This prospective study aimed to develop a model combining visual diameter stenosis (DS) and quantitative coronary angiography (QCA)-derived parameters to diagnose ischaemic lesions using invasive fractional flow reserve (FFR) with pharmacologically induced maximal hyperaemia as the gold standard.

A systematic review of Automated prediction of sudden cardiac deathusing ECG signals.

Sudden Cardiac Death (SCD) stands as a life-threatening cardiac event capable of swiftly claiming lives. Researchers have devised numerous models aimed at automatically predicting SCD through a combination of diverse feature extraction techniques and classifiers. We did a rigorous review of research publications ranging from 2011 to 2023, with a specific focus on the automated prediction of SCD, a growing health concern on a global scale.

Black-white hole pattern: an investigation on the automated chronic neuropathic pain detection using EEG signals.

Electroencephalography (EEG) signals provide information about the brain activities, this study bridges neuroscience and machine learning by introducing an astronomy-inspired feature extraction model. In this work, we developed a novel feature extraction function, black-white hole pattern (BWHPat) which dynamically selects the most suitable pattern from 14 options. We developed BWHPat in a four-phase feature engineering model, involving multileveled feature extraction, feature selection, classification, and cortex map generation.

Early prediction of sudden cardiac death using multimodal fusion of ECG Features extracted from Hilbert-Huang and wavelet transforms with explainable vision transformer and CNN models.

Background And Objective: Sudden cardiac death (SCD) is a critical health issue characterized by the sudden failure of heart function, often caused by ventricular fibrillation (VF). Early prediction of SCD is crucial to enable timely interventions. However, current methods predict SCD only a few minutes before its onset, limiting intervention time.

A Pragmatic Approach to Fetal Monitoring via Cardiotocography Using Feature Elimination and Hyperparameter Optimization.

Cardiotocography (CTG) is used to assess the health of the fetus during birth or antenatally in the third trimester. It concurrently detects the maternal uterine contractions (UC) and fetal heart rate (FHR). Fetal distress, which may require therapeutic intervention, can be diagnosed using baseline FHR and its reaction to uterine contractions.

A cohort study on the predictive capability of body composition for diabetes mellitus using machine learning.

Purpose: We applied machine learning to study associations between regional body fat distribution and diabetes mellitus in a population of community adults in order to investigate the predictive capability. We retrospectively analyzed a subset of data from the published Fasa cohort study using individual standard classifiers as well as ensemble learning algorithms.

Methods: We measured segmental body composition using the Tanita Analyzer BC-418 MA (Tanita Corp, Japan).

Role of anticoagulation in non-ST-elevation myocardial infarction: a contemporary narrative review.

Introduction: Anticoagulants play a vital role as part of the antithrombotic therapy of myocardial infarction and are complementary to antiplatelet therapies. In the acute setting, the rationale for their use is to antagonize the ongoing clotting cascade including during percutaneous coronary intervention. Anticoagulation may be an important part of the longer-term antithrombotic strategy especially in patients who have other existing indications (e.

Effects of short-term moderate intensity exercise on the serum metabolome in older adults: a pilot randomized controlled trial.

Background: We previously reported changes in the serum metabolome associated with impaired myocardial relaxation in an asymptomatic older community cohort. In this prospective parallel-group randomized control pilot trial, we subjected community adults without cardiovascular disease to exercise intervention and evaluated the effects on serum metabolomics.

Methods: Between February 2019 to November 2019, thirty (83% females) middle-aged adults (53 ± 4 years) were randomized with sex stratification to either twelve weeks of moderate-intensity exercise training (Intervention) (n = 15) or Control (n = 15).

ELRL-MD: a deep learning approach for myocarditis diagnosis using cardiac magnetic resonance images with ensemble and reinforcement learning integration.

Myocarditis poses a significant health risk, often precipitated by viral infections like coronavirus disease, and can lead to fatal cardiac complications. As a less invasive alternative to the standard diagnostic practice of endomyocardial biopsy, which is highly invasive and thus limited to severe cases, cardiac magnetic resonance (CMR) imaging offers a promising solution for detecting myocardial abnormalities.This study introduces a deep model called ELRL-MD that combines ensemble learning and reinforcement learning (RL) for effective myocarditis diagnosis from CMR images.

Going beyond summed stress scores: Correlating global and territorial coronary flow reserve by single photon emission tomography with routine myocardial perfusion imaging.

A Background: Technological advancement in the recent years has enabled the application of single photon emission tomography (SPECT) to evaluate myocardial blood flow (MBF). This method offers increased sensitivity in the assessment of coronary health, quantifiable through non-invasive imaging beyond the more conventional methods such as with myocardial perfusion imaging (MPI).

B Aims: To correlate MBF, derived by dynamic SPECT, both global and by coronary territories to the summed stress scores (SSS) on conventional MPI.

Current frailty knowledge, awareness, and practices among physicians following the 2022 European consensus document on Frailty in Cardiology.

Aims: Aging-related cardiovascular disease and frailty burdens are anticipated to rise with global aging. In response to directions from major cardiovascular societies, we investigated frailty knowledge, awareness, and practices among cardiologists as key stakeholders in this emerging paradigm a year after the European Frailty in Cardiology consensus document was published.

Methods And Results: We launched a prospective multinational web-based survey via social networks to broad cardiology communities representing multiple World Health Organization regions, including Western Pacific and Southeast Asia regions.

β-blockers are not all the same: pharmacologic similarities and differences, potential combinations and clinical implications.

β-blockers are a heterogeneous class, with individual agents distinguished by selectivity for β- vs. β- and α-adrenoceptors, presence or absence of partial agonist activity at one of more β-receptor subtype, presence or absence of additional vasodilatory properties, and lipophilicity, which determines the ease of entry the drug into the central nervous system. Cardioselectivity (β-adrenoceptor selectivity) helps to reduce the potential for adverse effects mediated by blockade of β-adrenoceptors outside the myocardium, such as cold extremities, erectile dysfunction, or exacerbation of asthma or chronic obstructive pulmonary disease.

FLP: Factor lattice pattern-based automated detection of Parkinson's disease and specific language impairment using recorded speech.

Background: Timely detection of neurodevelopmental and neurological conditions is crucial for early intervention. Specific Language Impairment (SLI) in children and Parkinson's disease (PD) manifests in speech disturbances that may be exploited for diagnostic screening using recorded speech signals. We were motivated to develop an accurate yet computationally lightweight model for speech-based detection of SLI and PD, employing novel feature engineering techniques to mimic the adaptable dynamic weight assignment network capability of deep learning architectures.

Longitudinal aortic strain, ventriculo-arterial coupling and fatty acid oxidation: novel insights into human cardiovascular aging.

Aging-induced aortic stiffness has been associated with altered fatty acid metabolism. We studied aortic stiffness using cardiac magnetic resonance (CMR)-assessed ventriculo-arterial coupling (VAC) and novel aortic (AO) global longitudinal strain (GLS) combined with targeted metabolomic profiling. Among community older adults without cardiovascular disease, VAC was calculated as aortic pulse wave velocity (PWV), a marker of arterial stiffness, divided by left ventricular (LV) GLS.

Characteristics of pulmonary artery strain assessed by cardiovascular magnetic resonance imaging and associations with metabolomic pathways in human ageing.

Background: Pulmonary artery (PA) strain is associated with structural and functional alterations of the vessel and is an independent predictor of cardiovascular events. The relationship of PA strain to metabolomics in participants without cardiovascular disease is unknown.

Methods: In the current study, community-based older adults, without known cardiovascular disease, underwent simultaneous cine cardiovascular magnetic resonance (CMR) imaging, clinical examination, and serum sampling.

Automated anxiety detection using probabilistic binary pattern with ECG signals.

Background And Aim: Anxiety disorder is common; early diagnosis is crucial for management. Anxiety can induce physiological changes in the brain and heart. We aimed to develop an efficient and accurate handcrafted feature engineering model for automated anxiety detection using ECG signals.

Heart-brain mapping: Cardiac atrial function is associated with distinct cerebral regions with high free water in older adults.

Left atrial (LA) dysfunction has been linked to cognitive impairment and cerebrovascular dysfunction. Higher brain free-water (FW) derived from diffusion-MRI was associated with early and subtle cerebrovascular dysfunction and more severe cognitive impairment. We hypothesized that LA dysfunction would correlate with higher brain free-water (FW) among healthy older adults.

Assessment of right ventricular diastolic function in pediatric patients with repaired tetralogy of Fallot by cardiovascular magnetic resonance and echocardiography.

Objectives: Cardiovascular magnetic resonance (CMR) imaging is routinely performed for assessing right ventricular (RV) systolic but not diastolic function. We aimed to investigate CMR-based assessment of RV diastolic function in pediatric patients with repaired tetralogy of Fallot (rTOF), compared to transthoracic echocardiography (TTE) measurements.

Methods: A total of 130 consecutive pediatric patients with rTOF who underwent clinically indicated CMR and same-day TTE were included.