抽象的な

Development and evaluation of a new artificial intelligence model for the analysis of electrocardiographic abnormalities.

Diandro Marinho Mota¹, Fabiano Barcellos Filho², Amanda Bergamo Mazetto², Marlon Woelffel Candoti², Jose Henrique Lopes²

Cardiovascular disease has reached alarming numbers in the last few years. However, there was always a need for technologies that help in the diagnosis and screening of diseases, but there is still a need to increase confidence in Artificial Intelligence (AI) models. Research with signal extraction and processing has high accuracy in detecting cardiac anomalies, although Deep Learning models have been better. Our objective is to develop an AI model to detect high sensitivity anomalies compared to medical reports. We used image processing with signal extraction and processing from digitized electrocardiograms using the rules of the Brazilian Directive on Analysis and Emission of Electrocardiograms. The AI model scored 96% on sensitivity and 26.9% on specificity, with an F1 of 0.83, resulting in a great AI for case screening. We conclude that AI models, in which we use the ECG standards for classification, can be included in the arsenal of predictive methods for screening with high sensitivity and bring interpretability to complement the most efficient algorithms.