Cardiologs

The first ECG analysis solution powered by Artificial Intelligence.

A matter of time

Ambulatory ECG analysis and reporting is a labor-intensive process, resulting in important costs and delays for arrhythmia detection.

By applying the latest research in Artificial intelligence, Cardiologs provides a web solution to streamline this process at large scale, enabling the highest diagnostic yield, for the least physician effort.

Now FDA cleared and CE marked, Cardiologs can read ECG recordings from any digital device. Take an ECG signal, upload it to the Cardiologs cloud and get to the report in a few clicks!

Measurements:HR 76bpm
Characteristics:Absence of P waves, irregular QRS waves
Prediction:Atrial Fibrillation
Severity level:Medium

Out soon

Cardiologs’ Analysis Solution is coming soon.
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Designed for accuracy

Expert cardiologists are able to interpret an ECG in a single glance, with an ease comparable to recognising a close friend.

To reproduce this process, we trained a neural network on over 500,000 ECGs. The result is a system able to recognise patterns in the cardiac signal in the same intuitive manner as human experts do.

Our partners

bpi
startx
Pierre Taboulet
Pierre Taboulet
Pierre Taboulet
bpi
startx

Our leadership team

Yann Fleureau
Yann Fleureau
Executive
Before founding Cardiologs, Yann graduated from the École Polytechnique & UC Berkeley. Passionate about new technologies and medicine, he can spend hours with cardiologists talking about the future of AI in their practice.
Jia Li
Jia Li
Science
A graduate in Applied Mathematics from École Polytechnique and author of several articles, Jia runs the R&D team at Cardiologs. If you meet him when he’s not designing a new RCNN, you are most likely in Hyrule.
Pierre Taboulet
Pierre Taboulet
Cardiology
Cardiologist, ex-director of the Emergency Department at the Saint-Louis Hospital in Paris and author of the bestselling book “L’ECG de A à Z”, Pierre has over 30 years of experience, as well as a deep passion for the ECG: check his facebook page and his youtube channel!

Our Publications

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Li, J. (2016) Deep neural networks improve atrial fibrillation detection in Holter: first results. European Journal of Preventive Cardiology 23 (2S), 41.

Smith, S. W. (2017) Improved Interpretation of Atrial Dysrhythmias by a New Neural Network Electrocardiogram Interpretation Algorithm. Academic Emergency Medicine, 24 (S1), S235.

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