Date of Presentation

5-2-2024 12:00 AM

College

Rowan-Virtua School of Osteopathic Medicine

Poster Abstract

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and 1 on CA - meeting inclusion criteria of randomized control trials and clinical trials. Results: The results show the significant improvements in LEF diagnosis, increased AF and AVS detection, and accurate prediction of CA presence by AI-powered ECG. Conclusion: This review underscores the transformative potential of AI-powered ECGs in reshaping cardiovascular disease detection and management, emphasizing the need for ethical practices, and technological advancements.

Keywords

Artificial intelligence, ECG, Atrial fibrillation, Aortic valve stenosis, Low ejection fraction, Cardiac amyloidosis, Early Diagnosis

Disciplines

Bioethics and Medical Ethics | Biomedical Informatics | Cardiology | Cardiovascular Diseases | Diagnosis | Equipment and Supplies | Health Information Technology | Medicine and Health Sciences | Pathological Conditions, Signs and Symptoms | Radiology

Document Type

Poster

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May 2nd, 12:00 AM

Unveiling the Potential: The Role of AI-Enhanced ECG in Cardiovascular Disease Detection

Background: The Electrocardiogram (ECG) is a widely utilized, non-invasive, cost-effective cardiac test. Its integration with Artificial Intelligence (AI) has empowered it to become a potent screening tool and a predictor for various cardiovascular diseases, especially in asymptomatic individuals. Objective: This review investigates the utility of AI-powered ECG in early detection of cardiac conditions, focusing on conditions such as low ejection fraction (LEF), atrial fibrillation (AF), aortic valve stenosis (AVS), and cardiac amyloidosis (CA). Methods: A literature review spanning 2018 to 2024 was conducted, analyzing 10 articles - 3 on AF, 3 on AVS, 3 on LEF, and 1 on CA - meeting inclusion criteria of randomized control trials and clinical trials. Results: The results show the significant improvements in LEF diagnosis, increased AF and AVS detection, and accurate prediction of CA presence by AI-powered ECG. Conclusion: This review underscores the transformative potential of AI-powered ECGs in reshaping cardiovascular disease detection and management, emphasizing the need for ethical practices, and technological advancements.

 

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