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| http://www.ncbi.nlm.nih.gov/pmc/articles/PMC11479813 | PMC |
| http://dx.doi.org/10.1161/CIRCEP.124.012959 | DOI Listing |
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Another piece in the puzzle of atrial fibrillation risk: clinical, genetic, and electrocardiogram-based artificial intelligence.
Eur Heart J
December 2024
Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, MA, USA.
Electrocardiogram-based deep learning to predict mortality in paediatric and adult congenital heart disease.
Eur Heart J
October 2024
Department of Cardiology, Boston Children's Hospital, Boston, MA, USA.
Article Synopsis
- The study seeks to improve risk stratification for patients with congenital heart disease (CHD) by using an AI-enhanced electrocardiogram (ECG) tool, addressing a significant gap in current medical practice.* -
- A convolutional neural network was developed and tested on a large set of ECGs from patients at Boston Children's Hospital to predict 5-year mortality, showing good performance compared to traditional indicators like age and heart function metrics.* -
- The model displays potential for timely risk assessment across various ages and types of CHD, helping to guide future monitoring and therapeutic interventions for patients.*
Electrocardiogram-Based Deep Learning to Predict Mortality in Repaired Tetralogy of Fallot.
JACC Clin Electrophysiol
December 2024
Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts, USA; Department of Pediatrics, Harvard Medical School, Boston, Massachusetts, USA. Electronic address:
Background: Artificial intelligence-enhanced electrocardiogram (AI-ECG) analysis shows promise to predict mortality in adults with acquired cardiovascular diseases. However, its application to the growing repaired tetralogy of Fallot (rTOF) population remains unexplored.
Objectives: This study aimed to develop and externally validate an AI-ECG model to predict 5-year mortality in rTOF.
Electrocardiogram-Based Artificial Intelligence to Discriminate Cardioembolic Stroke and Stratify Risk of Atrial Fibrillation After Stroke.
Circ Arrhythm Electrophysiol
October 2024
Cardiovascular Disease Initiative (S. Khurshid, R.M., A.C.T., S.A.L., P.T.E., C.D.A.), Broad Institute of MIT and Harvard, Cambridge, MA.
Integrating Clinical, Genetic, and Electrocardiogram-Based Artificial Intelligence to Estimate Risk of Incident Atrial Fibrillation.
medRxiv
August 2024
Cardiovascular Disease Initiative, Broad Institute of Harvard and the Massachusetts Institute of Technology, Cambridge, Massachusetts, USA.
Background: AF risk estimation is feasible using clinical factors, inherited predisposition, and artificial intelligence (AI)-enabled electrocardiogram (ECG) analysis.
Objective: To test whether integrating these distinct risk signals improves AF risk estimation.
Methods: In the UK Biobank prospective cohort study, we estimated AF risk using three models derived from external populations: the well-validated Cohorts for Aging in Heart and Aging Research in Genomic Epidemiology AF (CHARGE-AF) clinical score, a 1,113,667-variant AF polygenic risk score (PRS), and a published AI-enabled ECG-based AF risk model (ECG-AI).
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