首页|Department of Cardiology Reports Findings in Artificial Intelligence (Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm)
Department of Cardiology Reports Findings in Artificial Intelligence (Single-lead electrocardiogram Artificial Intelligence model with risk factors detects atrial fibrillation during sinus rhythm)
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Oxford Univ Press
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Artificial Intelligence is the subject of a report. According to news reporting from Roeselare, Belgium, by NewsRx journalists, research stated, “Guidelines recommend oppor- tunistic screening for atrial fibrillation (AF), using a 30 s single-lead electrocardiogram (ECG) recorded by a wearable device. Since many patients have paroxysmal AF, identification of patients at high risk presenting with sinus rhythm (SR) may increase the yield of subsequent long-term cardiac monitoring.” The news correspondents obtained a quote from the research from the Department of Cardiology, “The aim is to evaluate an AI-algorithm trained on 10 s single-lead ECG with or without risk factors to predict AF. This retrospective study used 13 479 ECGs from AF patients in SR around the time of diagnosis and 53 916 age- and sex-matched control ECGs, augmented with 17 risk factors extracted from electronic health records. AI models were trained and compared using 1- or 12-lead ECGs, with or without risk factors. Model bias was evaluated by age- and sex-stratification of results. Random forest models identified the most relevant risk factors. The single-lead model achieved an area under the curve of 0.74, which increased to 0.76 by adding six risk factors (95% confidence interval: 0.74-0.79). This model matched the performance of a 12-lead model. Results are stable for both sexes, over ages ranging from 40 to 90 years. Out of 17 clinical variables, 6 were sufficient for optimal accuracy of the model: hypertension, heart failure, valvular disease, history of myocardial infarction, age, and sex. An AI model using a single-lead SR ECG and six risk factors can identify patients with concurrent AF with similar accuracy as a 12-lead ECG-AI model.” According to the news reporters, the research concluded: “An age- and sex-matched data set leads to an unbiased model with consistent predictions across age groups.” This research has been peer-reviewed.
RoeselareBelgiumEuropeArtificial IntelligenceAtrial Fibril- lationCardiac ArrhythmiasEmerging TechnologiesHealth and MedicineHeart DiseaseHeart Disorders and DiseasesMachine LearningRisk and Prevention
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NSTL
Oxford Univ Press
