Investigating automated regression models for estimating left ventricular ejection fraction levels in heart failure patients using circadian ECG features

SM Al Younis, LJ Hadjileontiadis, AM Al Shehhi… - Plos one, 2023 - journals.plos.org
Heart Failure (HF) significantly impacts approximately 26 million people worldwide, causing
disruptions in the normal functioning of their hearts. The estimation of left ventricular ejection …

Identification of congenital valvular murmurs in young patients using deep learning-based attention transformers and phonocardiograms

M Alkhodari, LJ Hadjileontiadis… - IEEE Journal of …, 2024 - ieeexplore.ieee.org
One in every four newborns suffers from congenital heart disease (CHD) that causes defects
in the heart structure. The current gold-standard assessment technique, echocardiography …

A systemic review and meta-analysis comparing the ability of diagnostic of the third heart sound and left ventricular ejection fraction in heart failure

L Dao, M Huang, X Lin, L Li, X Feng, C Wei… - Frontiers in …, 2022 - frontiersin.org
Objective This study aimed to compare the sensitivity and specificity of diagnosis between
the third heart sound (S3) and left ventricular ejection fraction (LVEF) in heart failure (HF) …

Prediction of fetal RR intervals from maternal factors using machine learning models

N Widatalla, M Alkhodari, K Koide, C Yoshida… - Scientific Reports, 2023 - nature.com
Previous literature has highlighted the importance of maternal behavior during the prenatal
period for the upbringing of healthy adults. During pregnancy, fetal health assessments are …

Prediction of heart failure patients with distinct left ventricular ejection fraction levels using circadian ECG features and machine learning

SM Al Younis, LJ Hadjileontiadis, AH Khandoker… - Plos one, 2024 - journals.plos.org
Heart failure (HF) encompasses a diverse clinical spectrum, including instances of transient
HF or HF with recovered ejection fraction, alongside persistent cases. This dynamic …

[HTML][HTML] Circadian assessment of heart failure using explainable deep learning and novel multi-parameter polar images

M Alkhodari, AH Khandoker, HF Jelinek… - Computer Methods and …, 2024 - Elsevier
Background and objective Heart failure (HF) is a multi-faceted and life-threatening syndrome
that affects more than 64.3 million people worldwide. Current gold-standard screening …

[HTML][HTML] Deep learning identifies cardiac coupling between mother and fetus during gestation

M Alkhodari, N Widatalla, M Wahbah… - Frontiers in …, 2022 - frontiersin.org
In the last two decades, stillbirth has been a major problem that caused around 2 million
fetal deaths worldwide. Although current ultrasound tools are reliably used for the …

Leveraging machine learning techniques to forecast chronic total occlusion before coronary angiography

Y Shi, Z Zheng, Y Liu, Y Wu, P Wang, J Liu - Journal of Clinical Medicine, 2022 - mdpi.com
Background: Chronic total occlusion (CTO) remains the most challenging procedure in
coronary artery disease (CAD) for interventional cardiology. Although some clinical risk …

Heart Failure Assessment Using Multiparameter Polar Representations and Deep Learning *

M Alkhodari, LJ Hadjileontiadis… - 2023 45th Annual …, 2023 - ieeexplore.ieee.org
Heart failure refers to the inability of the heart to pump enough amount of blood to the body.
Nearly 7 million people die every year because of its complications. Current gold-standard …

Deep learning for predicting rehospitalization in acute heart failure: Model foundation and external validation

MN Kim, YS Lee, Y Park, A Jung, H So, J Park… - ESC Heart …, 2024 - Wiley Online Library
Aims Assessing the risk for HF rehospitalization is important for managing and treating
patients with HF. To address this need, various risk prediction models have been developed …