Cardiologs, a global leader in artificial intelligence (AI) cardiology diagnostics, announced today that its latest study, “Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network,” has been published in the European Heart Journal - Digital Health.
| Newly published research shows that Cardiologs’ deep learning model can predict the short-term risk of atrial fibrillation (AFib) based on 24-hour Holter recordings that show normal sinus rhythm. PARIS and BOSTON, June 21, 2022 /PRNewswire/ -- Cardiologs, a global leader in artificial intelligence (AI) cardiology diagnostics, announced today that its latest study, “Short-term prediction of atrial fibrillation from ambulatory monitoring ECG using a deep neural network,” has been published in the European Heart Journal - Digital Health.
The study, led by Dr. Jagmeet Singh, Cardiologist at Massachusetts General Hospital (MGH) and Professor of Medicine at Harvard Medical School, consisted of training Cardiologs’ deep neural network to predict the near-term presence or absence of AFib by only using the first 24 hours of an extended Holter recording. Results showed that the network was able to predict whether AFib would occur in the near future with an area under the receiver operating curve, sensitivity, and specificity of 79.4%, 76%, and 69%, respectively, and outperformed ECG features previously shown to be predictive of AFib. These results showed a ten-point improvement compared to a baseline model using age and sex. Atrial Fibrillation (AFib) affects millions of people each year. However, the condition is often unrecognized and untreated. Nowadays, patients are subject to 24-hour ambulatory electrocardiograms (ECGs) to receive a diagnosis, but this short-duration recording is known to have a low diagnostic yield and misses many patients with infrequent AFib episodes. The Cardiologs’ study is the first of its kind to demonstrate the capability of artificial intelligence in predicting AFib in the short-term using 24-hour Holter compared to resting 12-lead ECGs. While 12-lead ECG gives access to a larger view of the hearts’ activity for a short period, 24-hour Holter provides longer-duration signals, therefore, offering additional inputs for predicting models. “Cardiologs’ study shows that 24-hour Holter data can be used to enhance current monitoring capabilities, bringing hope to high-risk patients who would benefit from proactive treatment and AFib mitigation strategies. By getting patients the care they need sooner and potentially preventing more severe outcomes, we could help save many lives,” said Dr. Jagmeet P. Singh. “The extension of AI capabilities towards predictions and digital biomarkers has the potential to bring improved health outcomes leading to new diagnostic paradigms. Predictive biomarkers may lead to early detection, optimized patient monitoring, and improved patient management in general. At Cardiologs, we are excited to be at the forefront of innovations that help set a new standard of patient care,” said Cardiologs CEO and co-founder Yann Fleureau. Read the full study here: https://cardiologs.com/wp-content/uploads/2022/06/Short-term-prediction-of-atrial-fibrillation-from-ambulatory-monitoring-ECG-using-a-deep-neural-network.pdf. About Cardiologs Cardiologs is a medical technology company committed to transforming cardiac diagnostics using medical-grade artificial intelligence and cloud technology. Developed in partnership with leading physicians, the Cardiologs Holter Platform empowers clinicians worldwide to deliver expert cardiac care faster and more efficiently. CE-Marked and FDA cleared for detection of 14 cardiac arrhythmias, the Cardiologs Holter Platform is built on a growing database of more than 20 million ECG recordings and is supported by a number of clinical publications. In November 2021, Cardiologs announced that they were being acquired by Philips to expand their cardiac portfolio. Media Contact:
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