PARIS--(BUSINESS WIRE)--Cardiologs Technologies SAS announced today that it has received FDA clearance of its Cardiologs ECG Analysis Platform, a cloud-based cardiac monitoring-analysis web service powered by artificial intelligence (AI). Cardiologs aids physicians in screening for atrial fibrillation (AFib) and other arrhythmias using long-term ambulatory ECG monitoring recordings. The Cardiologs system is also CE-marked in Europe.
Atrial fibrillation (AFib) is the most common human arrhythmia, affecting about 33 million patients worldwide. AFib is a growing problem in cardiovascular disease and is associated with an increased risk of severe stroke, heart failure, and death. AFib is often asymptomatic, with stroke as the first manifestation. Indeed, recent stroke registries indicate that AFib is associated with one-third of all ischemic strokes.
“It is intuitive that screening for AFib and subsequent anticoagulant treatment should reduce the stroke burden, which is the basis of guideline recommendations to screen for AFib in persons over the age of 65,” said Dr. Arnaud Rosier, cardiac electrophysiologist at the Hôpital Jacques Cartier, Massy. “Unfortunately, current R-R interval based methods to detect AFib are characterized by an inferior Positive Predictive Value (PPV) of under 59%, leading to misdiagnoses, mostly false positives, that add significant cost to the healthcare system while burdening healthcare resources and placing unnecessary stress on misdiagnosed patients or putting undiagnosed patients in harm’s way.”
“Cardiologs is a game-changer for arrhythmia screening,” added Yann Fleureau, co-founder and CEO of Cardiologs Technologies. “A cardiologist recovers a digital ECG from any compatible cardiac monitoring device—such as a Holter monitor, smartwatch, ECG patch or even a connected t-shirt—then uploads it to the Cardiologs cloud and is able to immediately leverage our technology to identify relevant events. It is especially powerful for long-term recordings that used to require a very laborious manual analysis process.
“Our Cardiologs team has trained a neural network using more than 500,000 recordings, and this training dataset keeps growing,” said Fleureau. “The result is that Cardiologs is designed to recognize patterns in a cardiac signal for fast and precise analysis of arrhythmias such as AFib in a similar intuitive manner as expert cardiologists. We especially bring the P-wave detection (atrial activity) to a whole new level, which explains our radically better PPV,” said Fleureau.
Scientific Validation of Cardiologs. When defining the reliability of diagnosing AFib and other arrhythmias, the term Positive Predictive Value (PPV) refers to the percentage of true positive cases among total cases detected. Conventional “state-of-the-art” PPV for detecting AFib is less than 59%1. The PPV for Cardiologs’ detection of AFib was 91%2 included in the cleared FDA submission. In addition, also as included in the cleared FDA submission, Cardiologs’ sensitivity for detecting AFib was reported to be 97%3 (the percentage of positive cases truly identified) and was superior to “state-of-the-art” conventional methods of detecting AFib and other arrhythmias. Cardiologs’ study results have been published in the European Journal of Preventive Cardiology (2016, Vol. 23(2S 41-55). The study’s investigators concluded: “This (Cardiologs) method may be more reliable and accurate than previous methods in the diagnosis of AFib on long-duration ambulatory ECG and other monitoring devices.”
About Cardiologs Technologies SAS
Headquartered in Paris, Cardiologs Technologies is a privately held company offering the “Cardiologs ECG Analysis” platform to aid cardiologists in screening for arrhythmias such as AFib using long-duration ambulatory ECG monitoring recordings.
1 Source: European Journal of Preventive Cardiology
(2016, Vol. 23(2S 41-55).
2 Duration PPV results from testing following the recognized consensus standards ANSI/AAMI EC57:2012 and IEC 60601-2-47:2012 on MIT-BIH database.
3 Duration Sensitivity results from testing following the recognized consensus standards ANSI/AAMI EC57:2012 and IEC 60601-2-47:2012 on MIT-BIH database.