Artificial Intelligence | News | Insights | AiThority
[bsfp-cryptocurrency style=”widget-18″ align=”marquee” columns=”6″ coins=”selected” coins-count=”6″ coins-selected=”BTC,ETH,XRP,LTC,EOS,ADA,XLM,NEO,LTC,EOS,XEM,DASH,USDT,BNB,QTUM,XVG,ONT,ZEC,STEEM” currency=”USD” title=”Cryptocurrency Widget” show_title=”0″ icon=”” scheme=”light” bs-show-desktop=”1″ bs-show-tablet=”1″ bs-show-phone=”1″ custom-css-class=”” custom-id=”” css=”.vc_custom_1523079266073{margin-bottom: 0px !important;padding-top: 0px !important;padding-bottom: 0px !important;}”]

Study Demonstrates Cardiologs’ AI Can Predict Occurrence of Atrial Fibrillation in the Near Future

Cardiologs, a global leader in artificial intelligence (AI) cardiology diagnostics, announced 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.

Recommended AI News: IXL Learning Acquires Language Learning Software Developer Curiosity Media

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.

Related Posts
1 of 29,185

Recommended AI News: PingCAP’s TiDB Cloud Launches on Google Cloud Marketplace to Reach Developers Globally

“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.

Recommended AI News: Telefónica Tech Joins Forces with Netskope to Bring Cloud Security Solutions to the Corporate Environment

[To share your insights with us, please write to]

Comments are closed.