No need for a cardiologist for AMI diagnosis – progress of transforming a behemoth telemedicine program with artificial intelligence. (3rd October 2022)

• Record Type: Journal Article
• Authors: Mehta, S; Vieira, D; Zerpa, D; Guillen, V; Carrasquel, M; Ramadan, S; Martinez, F; Rossitto, F; Carrera, K; Fleming, M; Pinos, D; Brena-Pastor, L; Ozair, S; Gonzalez, A; Barco, A
• Is Part Of: European heart journal. Volume 43(2022)Supplement 2
• Publication Date: 2022-10-03
• Subjects: Cardiology -- Periodicals; Heart -- Diseases -- Periodicals; 616.12005
• DOI: 10.1093/eurheartj/ehac544.2251
• Languages: English
• ISSNs: 0195-668X

Abstract

Abstract: Background: The Latin American Telemedicine Infarct Network (LATIN) Telemedicine is a mammoth hub and spoke model that provides an umbrella of AMI protection for 100 million patients. In the program, 826, 043 patients had a telemedicine encounter; 7, 400 with AMI were diagnosed; 4, 332 of them managed with guidelines-based strategies. We have gradually begun implementing a system for using Artificial Intelligence (AI) algorithms embedded into EKGs for rapid and accurate STEMI detection and validated the results with a cardiologist's interpretations. Purpose: To test whether an AI-driven EKG algorithm can effectively substitute a cardiologist for STEMI telemedicine protocols. Methods: The AI algorithm construction was in the following fashion. Sample: a selection of 8, 511 EKG and 90, 592 classified heartbeats. Pre-processing: segmentation of each EKG into individual heartbeats. Training & testing: 90% and 10% of the total dataset, respectively. Classification: 1-D Convolutional Neural Network; the study constructed classes for each heartbeat. The algorithm was next deployed on a consecutive series of LATIN EKG records to diagnose STEMI. We afterwards compared the algorithm's results with eight expert cardiologists' interpretations of the same sample. Results: This study achieved a concordance of 91% between the AI algorithm and cardiologist interpretation (Figure 1). Conclusions: The initial results with AI algorithms for STEMI diagnosis are encouraging and may provide the base work for new tools for cardiologists to improve their efficiency. Moreover, implementing this innovative tool may overcome current limitations associated with the telemedical management of this disease. Funding Acknowledgement: Type of funding sources: None.