Enriching artificial intelligence ST-elevation myocardial infarction (STEMI) detection algorithms with differential diagnoses. (25th November 2020) - Mehta, S; Niklitschek, S; Fernandez, F; Villagran, C; Escobar, E; Avila, J; Cardenas, G; Rocuant, R; Vera, F; Frauenfelder, A; Vieira, D; Quintero, S; et al.
Abstract: Background: STEMI outcomes, although improved with systems of care, are hamstrung by delayed presentation and prevaricates of a 12-lead ECG. We report an artificial intelligence (AI) guided, single lead EKG algorithm for a self-administered tool to reliably detect STEMI and trigger ambulance dispatch. Purpose: To provide a reliable and improved AI-guided Single Lead EKG methodology. Methods: From our cardiologist-annotated repository, we assigned a dataset of 11, 118 classified ECG. Ontology organized 5 groups apportioned for an interclass balance among commoner STEMI differential diagnoses. This anonymous, pre-classified data included 5, 549 STEMI, 1, 391 normal, 1, 393 Bundle Branch Block, 1, 393 non-specific ST-T changes and 1, 392 miscellaneous. Each ECG was fragmented into individual 1-lead strips. Algorithm: 1-D Convolutional Neural Networks. Gender and age were included before the last dense layer. Training and Testing: Preset 90% dataset (10, 008 ECG) train, 10% test (1, 110 ECG). Statistical Analysis and ROC curves: Digitized dataset, 500 samples/second, 10s duration, total 5, 000 samples per lead. Statistical mean for each lead was calculated and subtracted from the original lead. Statistical values and ROC curves were assessed. Results: Most Accurate: Lead V2 – 91%; Most Sensitive: Lead I – 92% Most Specific: Lead III – 96%. Best AUC: Lead V2 – 91%. Conclusions: Incorporating subtypes of STEMI differential diagnosis enriches the single lead AI algorithm. Validating the derived algorithm with our entire database of 18 million ECG will further strengthen the results. Funding Acknowledgement: Type of funding source: None