首页|Institut Cardiovasculaire Paris Sud Reports Findings in Artificial Intelligence (Artificial intelligence-based electrocardiogram analysis improves atrial arrhyt hmia detection from a smartwatch electrocardiogram)
Institut Cardiovasculaire Paris Sud Reports Findings in Artificial Intelligence (Artificial intelligence-based electrocardiogram analysis improves atrial arrhyt hmia detection from a smartwatch electrocardiogram)
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New research on Artificial Intelligenc e is the subject of a report. According to news originating from Massy, France, by NewsRx correspondents, research stated, "Smartwatch electrocardiograms (SW EC Gs) have been identified as a non-invasive solution to assess abnormal heart rhy thm, especially atrial arrhythmias (AAs) that are related to stroke risk. Howeve r, the performance of these tools is limited and could be improved with the use of deep neural network (DNN) algorithms, particularly for specific populations e ncountered in clinical cardiology practice." Our news journalists obtained a quote from the research from Institut Cardiovasc ulaire Paris Sud, "A total of 400 patients from the electrophysiology department of one tertiary care hospital were included in two similar clinical trials (res pectively, 200 patients per study). Simultaneous ECGs were recorded with the wat ch and a 12-lead recording system during consultation or before and after an ele ctrophysiology procedure if any. The SW ECGs were processed by using the DNN and with the Apple watch ECG software (Apple app). Corresponding 12-lead ECGs (12L ECGs) were adjudicated by an expert electrophysiologist. The performance of the DNN was assessed vs. the expert interpretation of the 12L ECG, and inconclusive rates were reported. Overall, the DNN and the Apple app presented, respectively, a sensitivity of 91% [95 % confid ence interval (CI) 85-95%] and 61% (95% CI 44-75%) with a specificity of 95% (95% CI 91-97 %) and 97% (95% CI 93-99%) when compared with the physician 12L ECG interpretation. The DNN was able to provide a diagnosis on 99% of ECGs, while the Apple app was able to classify only 78% of strips (22% of inconclusive diagnosis)."
MassyFranceEuropeArrhythmiaArtif icial IntelligenceCardiologyEmerging TechnologiesHealth and MedicineMach ine Learning
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