Moving in sync – concordance betweena artificial intelligence and cardiologist on detecting normal electrocardiograms. (25th November 2020)

• Record Type: Journal Article
• Authors: Mehta, S; Avila, J; Villagran, C; Fernandez, F; Niklitschek, S; Vera, F; Rocuant, R; Cardenas, G; Escobar, E; Frauenfelder, A; Vieira, D; Vijayan, Y; Pinto, G; Ceschim, M; Luna, M
• Is Part Of: European heart journal. Volume 41:(2020)Supplement 2
• Publication Date: 2020-11-25
• Subjects: Acute Coronary Syndromes - Diagnostic Methods; Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/ehjci/ehaa946.1664
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: Merging modern technologies with classic diagnostic tests often results in a sense of insecurity within the medical community, particularly so with potentially life-saving studies such as the electrocardiogram (EKG). In order to provide a greater sense of trust between Artificial Intelligence (AI) and cardiologists, we provide an AI-driven algorithm capable of accurately and reliably characterize an EKG as normal within a highly complex, cardiologist-reviewed EKG database and report the degree of concordance between this machine vs physician scenario. Purpose: To provide a dependable and accurate AI algorithm that conducts EKG interpretation in a cardiologist-tier manner. Methods: The International Telemedical System (ITMS) developed and tested an EKG assessing AI algorithm and incorporated it into the workflow of their Telemedicine Integrated Platform, a digital EKG reading program where cardiologists continuously report their findings remotely in real-time. During the month of April 2, 019; 35 ITMS cardiologists reported a grand total of 61, 441 EKG records, later submitting them to the AI algorithm implemented through the "One Click Report" process. Through this simple 2-step approach, the algorithm provides a suggestion of "Normal" or "Abnormal" to the cardiologist based on the patterns of the fiducial points included in said EKG reports. Confirmation of these suggestions by the cardiologists ensued. Results: Overall, cardiologists confirmed 23, 213 out of 25, 013 AI outputs for "Normal" EKGs, demonstrating a concordance of 92.8% for Normal diagnosis. Conclusion: Through this methodology, we provide an AI technology that can be reliably applied and trusted in EKG digital platforms to identify and suitably label a normal EKG. Further testing will accrue into a multi label algorithm compatible with abnormal cardiovascular entities, potentially precluding the role of the cardiologist for triaging, particularly in the prehospital setting. We anticipate that this approach will become a promising methodology in modern cardiology practice. Funding Acknowledgement: Type of funding source: None