Deriving novel atrial fibrillation phenotypes using a tree-based artificial intelligence-enhanced electrocardiography approach
Mehak Gurnani(Imperial College London), Fu Siong Ng(Lung Institute), Riyaz Somani(University of Leicester), Joseph Barker(University of Leicester), Konstantinos Patlatzoglou(Imperial College London), Libor Pastika(Imperial College London), Daniel Kramer(Sanofi (Germany)), Arunashis Sau(Imperial College Healthcare NHS Trust), Declan P. O’Regan(MRC London Institute of Medical Sciences), Lara Curran(MRC London Institute of Medical Sciences), Jonathan W. Waks(Beth Israel Deaconess Medical Center), Nicholas S. Peters(St Mary's Hospital), Boroumand Zeidaabadi(Imperial College London), Ibrahim Antoun(Kettering General Hospital), Paolo Inglese(Istituto Nazionale di Fisica Nucleare, Sezione di Bari), G. Andre Ng(Glenfield Hospital)
Cited by 1
Related Papers
Anatomically Constrained Neural Networks (ACNNs): Application to Cardiac Image Enhancement and Segmentation
|IEEE Transactions on Medical Imaging|2017|738
Disturbed Connexin43 Gap Junction Distribution Correlates With the Location of Reentrant Circuits in the Epicardial Border Zone of Healing Canine Infarcts That Cause Ventricular Tachycardia
|Circulation|1997|580
Integrated allelic, transcriptional, and phenomic dissection of the cardiac effects of titin truncations in health and disease
|Science Translational Medicine|2015|496
Titin-truncating variants affect heart function in disease cohorts and the general population
|Nature Genetics|2016|367