Artificial intelligence and heart failure: A state‐of‐the‐art review

MS Khan, MS Arshad, SJ Greene… - European Journal of …, 2023 - Wiley Online Library
Heart failure (HF) is a heterogeneous syndrome affecting more than 60 million individuals
globally. Despite recent advancements in understanding of the pathophysiology of HF, many …

Effect of age and gender on the QTc-interval in healthy individuals and patients with long-QT syndrome

AS Vink, SAB Clur, AAM Wilde, NA Blom - Trends in cardiovascular …, 2018 - Elsevier
Age-and gender-related differences in QTc-interval are most likely the result of changes in
sex-specific hormones. Although the exact mechanisms and pathophysiology of sex …

Assessing and mitigating bias in medical artificial intelligence: the effects of race and ethnicity on a deep learning model for ECG analysis

PA Noseworthy, ZI Attia, LPC Brewer… - Circulation …, 2020 - Am Heart Assoc
Background: Deep learning algorithms derived in homogeneous populations may be poorly
generalizable and have the potential to reflect, perpetuate, and even exacerbate …

Automatic triage of 12‐lead ECGs using deep convolutional neural networks

RR van de Leur, LJ Blom, E Gavves, IE Hof… - Journal of the …, 2020 - Am Heart Assoc
BACKGROUND The correct interpretation of the ECG is pivotal for the accurate diagnosis of
many cardiac abnormalities, and conventional computerized interpretation has not been …

The influence of age and sex on the electrocardiogram

PW Macfarlane - Sex-Specific Analysis of Cardiovascular Function, 2018 - Springer
The electrocardiogram (ECG) remains the most commonly used test in medical practice and
as such requires to be interpreted with due care and attention to detail. The ECG changes …

Normal limits of the electrocardiogram derived from a large database of Brazilian primary care patients

DMF Palhares, MS Marcolino, TMM Santos… - BMC cardiovascular …, 2017 - Springer
Background Knowledge of the normal limits of the electrocardiogram (ECG) is mandatory for
establishing which patients have abnormal ECGs. No studies have assessed the reference …

[HTML][HTML] Big data and artificial intelligence: opportunities and threats in electrophysiology

RR van de Leur, MJ Boonstra, A Bagheri… - Arrhythmia & …, 2020 - ncbi.nlm.nih.gov
The combination of big data and artificial intelligence (AI) is having an increasing impact on
the field of electrophysiology. Algorithms are created to improve the automated diagnosis of …

Population‐based values and abnormalities of the electrocardiogram in the general Dutch population: the lifelines cohort study

MY van der Ende, JE Siland, H Snieder… - Clinical …, 2017 - Wiley Online Library
Background Our aim is to present average values and prevalence of electrocardiographic
(ECG) abnormalities among the general Dutch population in the LifeLines Cohort …

Automated ecg interpretation—a brief history from high expectations to deepest networks

PW Macfarlane, J Kennedy - Hearts, 2021 - mdpi.com
This article traces the development of automated electrocardiography from its beginnings in
Washington, DC around 1960 through to its current widespread application worldwide …

CoAt-Mixer: Self-attention deep learning framework for left ventricular hypertrophy using electrocardiography

JS Ryu, S Lee, Y Chu, MS Ahn, YJ Park, S Yang - Plos one, 2023 - journals.plos.org
Left ventricular hypertrophy is a significant independent risk factor for all-cause mortality and
morbidity, and an accurate diagnosis at an early stage of heart change is clinically …