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Nature Medicine|May 28, 2024
Noninvasive assessment of organ-specific and shared pathways in multi-organ fibrosis using T1 mappingVictor Nauffal, Marcus D R Klarqvist, Matthew C Hill, et al.
Circulation|November 8, 2021
ECG-Based Deep Learning and Clinical Risk Factors to Predict Atrial FibrillationShaan Khurshid, Samuel Friedman, Christopher Reeder, et al.
Circulation. Genomic and Precision Medicine|June 6, 2023
Genetic Susceptibility to Atrial Fibrillation Identified via Deep Learning of 12-Lead ElectrocardiogramsXin Wang, Shaan Khurshid, Seung Hoan Choi, et al.
Cell Genomics|December 27, 2021
Machine learning enables new insights into genetic contributions to liver fat accumulationMary E Haas, James P Pirruccello, Samuel N Friedman, et al.
Nature Communications|May 21, 2024
Deep learning of left atrial structure and function provides link to atrial fibrillation riskJames P Pirruccello, Paolo Di Achille, Seung Hoan Choi, et al.
Nature Genetics|April 20, 2023
Genetics of myocardial interstitial fibrosis in the human heart and association with diseaseVictor Nauffal, Paolo Di Achille, Marcus D R Klarqvist, et al.
NPJ Digital Medicine|January 11, 2025
Unsupervised deep learning of electrocardiograms enables scalable human disease profilingSam F Friedman, Shaan Khurshid, Rachael A Venn, et al.
European Journal of Preventive Cardiology|October 5, 2023
Deep learned representations of the resting 12-lead electrocardiogram to predict at peak exerciseShaan Khurshid, Timothy W Churchill, Nathaniel Diamant, et al.
Circulation. Heart Failure|November 16, 2024
Natural Language Processing to Adjudicate Heart Failure Hospitalizations in Global Clinical TrialsPablo M Marti-Castellote, Christopher Reeder, Brian Claggett, et al.
Cardiovascular Digital Health Journal|September 1, 2022
Deep learning on resting electrocardiogram to identify impaired heart rate recoveryNathaniel Diamant, Paolo Di Achille, Lu-Chen Weng, et al.
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