AIMC Topic: Cardiologists
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Impact of automatic acquisition of key clinical information on the accuracy of electrocardiogram interpretation: a cross-sectional study.
BMC medical education
Dec 8, 2023
BACKGROUND: The accuracy of electrocardiogram (ECG) interpretation by doctors are affected by the available clinical information. However, having a complete set of clinical details before making a diagnosis is very difficult in the clinical setting e...
Blinded, randomized trial of sonographer versus AI cardiac function assessment.
Nature
Apr 5, 2023
Artificial intelligence (AI) has been developed for echocardiography, although it has not yet been tested with blinding and randomization. Here we designed a blinded, randomized non-inferiority clinical trial (ClinicalTrials.gov ID: NCT05140642; no o...
Deep Learning and Artificial Intelligence: What Does the Cardiologist Really Need to Know?
Circulation. Cardiovascular imaging
Sep 20, 2022
Explaining deep neural networks for knowledge discovery in electrocardiogram analysis.
Scientific reports
May 26, 2021
Deep learning-based tools may annotate and interpret medical data more quickly, consistently, and accurately than medical doctors. However, as medical doctors are ultimately responsible for clinical decision-making, any deep learning-based prediction...
Computer versus cardiologist: Is a machine learning algorithm able to outperform an expert in diagnosing a phospholamban p.Arg14del mutation on the electrocardiogram?
Heart rhythm
Sep 8, 2020
BACKGROUND: Phospholamban (PLN) p.Arg14del mutation carriers are known to develop dilated and/or arrhythmogenic cardiomyopathy, and typical electrocardiographic (ECG) features have been identified for diagnosis. Machine learning is a powerful tool us...
Humans
Machine Learning
Algorithms
Calcium-Binding Proteins
Arrhythmogenic Right Ventricular Dysplasia
Male
Cardiologists
Middle Aged
Electrocardiography
Female
Clinical Competence
DNA
Adolescent
Mutation
Computers
Reproducibility of Results
DNA Mutational Analysis
Phenotype
Young Adult
Retrospective Studies
Adult
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural network.
Nature medicine
May 11, 2020
The electrocardiogram (ECG) is a widely used medical test, consisting of voltage versus time traces collected from surface recordings over the heart. Here we hypothesized that a deep neural network (DNN) can predict an important future clinical event...
Usefulness of Machine Learning-Based Detection and Classification of Cardiac Arrhythmias With 12-Lead Electrocardiograms.
The Canadian journal of cardiology
Mar 5, 2020
BACKGROUND: Deep-learning algorithms to annotate electrocardiograms (ECGs) and classify different types of cardiac arrhythmias with the use of a single-lead ECG input data set have been developed. It remains to be determined whether these algorithms ...
Cardiologist-level arrhythmia detection and classification in ambulatory electrocardiograms using a deep neural network.
Nature medicine
Jan 7, 2019
Computerized electrocardiogram (ECG) interpretation plays a critical role in the clinical ECG workflow. Widely available digital ECG data and the algorithmic paradigm of deep learning present an opportunity to substantially improve the accuracy and s...
The Artificial Intelligence Act approved by the EU: the difficult dialogue between the black box and the cardiologist.
European heart journal
Aug 9, 2024
Interventional cardiologists' perspectives and knowledge towards artificial intelligence.
The Journal of invasive cardiology
Aug 1, 2024
BACKGROUND: Artificial intelligence (AI) is increasingly utilized in interventional cardiology (IC) and holds the potential to revolutionize the field.
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