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The International Journal of Cardiovascular Imaging|February 24, 2022
Generalizability and quality control of deep learning-based 2D echocardiography segmentation models in a large clinical datasetXiaoyan Zhang, Alvaro E Ulloa Cerna, Joshua V Stough, et al.Circulation|May 9, 2022
rECHOmmend: An ECG-Based Machine Learning Approach for Identifying Patients at Increased Risk of Undiagnosed Structural Heart Disease Detectable by EchocardiographyAlvaro E Ulloa-Cerna, Linyuan Jing, John M Pfeifer, et al.Journal of Electrocardiology|November 27, 2022
An ECG-based machine learning model for predicting new-onset atrial fibrillation is superior to age and clinical features in identifying patients at high stroke riskSushravya Raghunath, John M Pfeifer, Christopher R Kelsey, et al.JACC. Heart Failure|May 11, 2020
A Machine Learning Approach to Management of Heart Failure PopulationsLinyuan Jing, Alvaro E Ulloa Cerna, Christopher W Good, et al.Nature Biomedical Engineering|February 9, 2021
Deep-learning-assisted analysis of echocardiographic videos improves predictions of all-cause mortalityAlvaro E Ulloa Cerna, Linyuan Jing, Christopher W Good, et al.Nature Medicine|May 13, 2020
Prediction of mortality from 12-lead electrocardiogram voltage data using a deep neural networkSushravya Raghunath, Alvaro E Ulloa Cerna, Linyuan Jing, et al.Circulation|February 16, 2021
Deep Neural Networks Can Predict New-Onset Atrial Fibrillation From the 12-Lead ECG and Help Identify Those at Risk of Atrial Fibrillation-Related StrokeSushravya Raghunath, John M Pfeifer, Alvaro E Ulloa-Cerna, et al.Pageof 1