Salah Al-Zaiti

Assistant Professor

University of Pittsburgh

United States

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About

Dr. Salah Al-Zaiti is an Assistant Professor at the University of Pittsburgh, United States. His research focuses on the application of machine learning and artificial intelligence in cardiology, particularly in areas such as electrocardiogram analysis and risk stratification for coronary artery disease. His recent publications explore innovative diagnostic tools and methods for monitoring heart conditions, including the use of deep learning architectures in electrocardiography.

Articles (19)

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Collaborators (2)

Stephanie Frisch

University of Pittsburgh

UNITED STATES

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Murat Akcakaya

University of Pittsburgh

UNITED STATES

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Salah Al-Zaiti

Research Interests

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About

Articles (19)

ECG-SMART-NET: A Deep Learning Architecture for Precise ECG Diagnosis of Occlusion Myocardial Infarction

A 12-Lead Electrocardiography Patch for Continuous Arrhythmia Monitoring: A Case Example

Electrocardiogram-based machine learning for risk stratification of patients with suspected acute coronary syndrome

Lymphocyte-based inflammatory markers: Novel predictors of significant coronary artery disease✰,✰✰

Association Between Persistent Hypothermia After Cardiopulmonary Bypass in Neonates and Odds of Serious Complications

Clinical usability of deep learning-based saliency maps for occlusion myocardial infarction identification from the prehospital 12-Lead electrocardiogram

The prognostic value of HEART score in patients with known heart failure evaluated at the emergency department for acute chest pain

Use of Artificial Intelligence in Improving Outcomes in Heart Disease: A Scientific Statement From the American Heart Association

Wide QRS Complex Rhythm in an Unresponsive Community-Based Patient

An annotated ventricular tachycardia (VT) alarm database: Toward a uniform standard for optimizing automated VT identification in hospitalized patients

Arrhythmia diagnosis and the 12-lead electrocardiogram: seeing the whole picture

Electrical disturbance from a systemic disease

Exercise-related acute cardiovascular events and potential deleterious adaptations following long-term exercise training: Placing the risks into perspective-an update: A scientific statement from the American Heart Association

Exploring the complex interactions of baseline patient factors to improve nursing triage of acute coronary syndrome

Identifying the most important ECG predictors of reduced ejection fraction in patients with suspected acute coronary syndrome

Machine learning-based prediction of acute coronary syndrome using only the pre-hospital 12-lead electrocardiogram

Noteworthy electrocardiographic changes following pharmacologic treatment of COVID-19

Sinus rhythm with frequent funny-looking beats

The complexities of wide complex tachycardias

Collaborators (2)

Stephanie Frisch

Murat Akcakaya

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