M. Alhusseini - Electrophysiol & Arrhythmia Serv, Stanford Univ

基本信息

Career Trajectory

Bio

Mahmood Alhusseini received the B.Sc. (Hons.) degree in mechanical engineering from Boston University, Boston, MA, USA, in 2011. He is currently an Engineer at the EXPEC Advanced Research Center, Saudi Aramco, Saudi Arabia, engaged in different fields including robotics, control, and energy. Mr. Alhusseini is a member of the mechanical engineering honor society, Pi Tau Sigma; the engineering honor society, Tau Beta Pi; and the Society of Petroleum Engineers.

Research Interests

Papers共 80 篇Author StatisticsCo-AuthorSimilar Experts

By YearBy Citation主题筛选期刊级别筛选合作者筛选合作机构筛选

Abstract 19210: Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction from the Surface Ecg

Rayan A Ansari,Neal Bhatia,James Tooley,Vyom Thakkar,Jessica Torres, Jagteshwar Tung,Mahmood Alhusseini, Paul L Clopton,Reza Sameni,Gari Clifford,Euan A Ashley,Marco V Perez,Matei Zaharia,Sanjiv M Narayan,Albert J Rogers

Circulationno. Suppl_1 (2023)

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Abstract 17420: Enhanced Identification of Cardiac Wall Motion Abnormalities: an Externally Validated Deep Neural Network Approach Outperforms Expert and Quantitative Analysis of Electrocardiograms

Albert J. Rogers,Neal Bhatia,James Tooley,Vyom Thakkar,Justin Xu,Rayan Ansari,Jessica Torres,Jagteshwar Tung,Mahmood Alhusseini,Paul L. Clopton,Reza Sameni,Gari Clifford,Euan A. Ashley,Marco V. Perez,Matei Zaharia,Sanjiv M. Narayan

CIRCULATION (2023)

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Interpretation of Deep Learning Model That Identifies Regional Myocardial Dysfunction From the Surface Ecg

Rayan A. Ansari,Neal Bhatia,James Tooley,Vyom Thakkar,Jessica Torres,Jagteshwar Tung,Mahmood Alhusseini,Paul L. Clopton,Reza Sameni,Gari Clifford,Euan A. Ashley,Marco V. Perez

CIRCULATION（2023）

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Atrial Fibrillation Ablation Outcome Prediction with a Machine Learning Fusion Framework Incorporating Cardiac Computed Tomography

Orod Razeghi,Ridhima Kapoor,Mahmood I. Alhusseini,Muhammad Fazal,Siyi Tang,Caroline H. Roney,Albert J. Rogers,Anson Lee,Paul J. Wang,Paul Clopton,Daniel L. Rubin,Sanjiv M. Narayan,Steven Niederer,Tina Baykaner

Journal of cardiovascular electrophysiology（2023）

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Abstract 18102: Impact of Regional Cardiac Wall Motion Abnormalities on Ejection Fraction and Mortality: Large Data Enabled by Natural Language Processing

Albert J. Rogers,Neal K. Bhatia,James Tooley,Vyom Thakkar,Justin Xu,Rayan Ansari,Jessica Torres,Jagteshwar Tung,Mahmood Alhusseini,Paul L. Clopton,Reza Sameni,Gari Clifford

CIRCULATION (2023)

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Performance of an Electrocardiographic Deep Learning Model for Detecting Wall Motion Abnormalities: an Analysis of Impact from Cardiovascular Comorbidities and Race in an External Validation Study

Rayan A. Ansari,Neal Bhatia,James Tooley,Vyom Thakkar,Justin Xu,Jessica Torres,Jagteshwar Tung,Mahmood Alhusseini,Paul L. Clopton,Reza Sameni,Gari Clifford,Euan A. Ashley

CIRCULATION (2023)

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Machine-learned physiological signatures from the ECG predict sudden death in ischemic cardiomyopathy

B Deb, A Selvalingam,M Alhusseini,A Rogers,P Ganesan,R Feng,P Clopton,S Ruiperez-Campillo,S Narayan

EP Europace（2022）

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Atrial fibrillation signatures on intracardiac electrograms identified by deep learning

Miguel Rodrigo,Mahmood I Alhusseini,Albert J Rogers,Chayakrit Krittanawong, Sumiran Thakur,Ruibin Feng,Prasanth Ganesan,Sanjiv M Narayan

Computers in biology and medicine（2022）

Cited3Views0WOSBibtex

DH-575-01 MACHINE LEARNING-ENABLED MULTIMODAL FUSION OF INTRA-ATRIAL AND BODY SURFACE SIGNALS IN PREDICTION OF ATRIAL FIBRILLATION ABLATION OUTCOMES

Siyi Tang,Orod Razeghi,Ridhima Kapoor,Mahmood Alhusseini,Muhammad Fazal,Albert J. Rogers,Miguel Rodrigo Bort,Paul Clopton,Paul J. Wang,Daniel L. Rubin,Sanjiv M. Narayan,Tina Baykaner

Heart rhythm（2022）

Cited10Views0WOSBibtex

Po-685-04 differing distribution of premature atrial complexes that do and do not precipitate spontaneous atrial fibrillation by non-invasive imaging

Miguel Rodrigo,Albert Joseph Rogers,Prasanth Ganesan,Brototo Deb,Mahmood I. Alhusseini,Sanjiv M. Narayan

Heart Rhythmno. 5 (2022): S377

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