Madrid, Jan 24.(Press Europa) -
Researchers at the Network Biomedical Research Center (CIBER) of cardiovascular diseases (CIBERCV) have applied artificial intelligence to electrocardiographic data analysis that has allowed defining improved biomarker profiles to predict the risk of atrial fibrillation or asymptomatic cases or asymptomatic cases.
The study, which has been published in the magazine 'Scientific Reports', has been carried out by members of the Princess University Hospital and the Autonomous University of Madrid (UAM).In this new study, researchers have comparatively evaluated six electrocardiographic analysis modes based on artificial intelligence, to identify subtle features that can anticipate episodes of atrial fibrillation.
To do this, they have analyzed data from a great cohort of 122.394 patients from the La Princesa de Madrid University Hospital."This analysis leads to the implementation of atrial fibrillation predictors improved by artificial intelligence, more reliable and reliable," said Cibercv researcher, Jesús Jiménez.
In addition, the study has also proven the performance of these models associated with data related to the age distribution of patients.In this sense, researchers have pointed out that "the age of patients is a key aspect to be found before applying artificial intelligence models to obtain significant results".
Specifically, they have clarified that the study confirms greater ease of predicting atrial fibrillation with these Big Data techniques in elderly and male patients.
"The possibility of identifying patients with subclinical atrial fibrillation or at high risk of developing it based on clinical risk scores or sinus rhythm electrocardiograms is very promising, and the ability of an automatic learning model to eliminate subjective interpretationopposable human errors can change the panorama of how these patients are handled, "they have affirmed.