ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial Fibrillation

Background: Artificial intelligence (AI)-enabled analysis of 12-lead ECGs may facilitate efficient estimation of incident atrial fibrillation (AF) risk. However, it remains unclear whether AI provides meaningful and generalizable improvement in predictive accuracy beyond clinical risk factors for AF...

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Bibliographic Details
Main Authors: Al-Alusi, M.A (Author), Anderson, C.D (Author), Batra, P. (Author), Di Achille, P. (Author), Diamant, N. (Author), Ellinor, P.T (Author), Foulkes, A.S (Author), Friedman, S. (Author), Harrington, L.X (Author), Ho, J.E (Author), Khurshid, S. (Author), Lubitz, S.A (Author), Philippakis, A.A (Author), Reeder, C. (Author), Sarma, G. (Author), Singh, P. (Author), Wang, X. (Author)
Format: Article
Language:English
Published: Lippincott Williams and Wilkins 2022
Subjects:
atrial fibrillation
Atrial Fibrillation
deep learning
Deep Learning
electrocardiography
Electrocardiography
electronic health records
female
Female
human
Humans
male
Male
middle aged
Middle Aged
pathology
procedures
risk factor
Risk Factors
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