Evaluation of an Ambulatory ECG Analysis Platform Using Deep Neural Networks in Routine Clinical Practice.

Laurent Fiorina¹, Carole Maupain², Christophe Gardella³, Vladimir Manenti¹, Fiorella Salerno¹, Pierre Socie², Jia Li³, Christine Henry³, Audrey Plesse³, Kumar Narayanan^4,5, Aurélie Bourmaud⁶, Eloi Marijon^4,7

¹Ramsay Santé Institut Cardiovasculaire Paris Sud, Hôpital privé Jacques Cartier Massy France.

²AP-HP, La Pitié Salpêtrière University Hospital, Cardiology Department Paris France.

³Cardiologs® Technologies Paris France.

⁴Université de Paris, PARCC, INSERM Paris France.

⁵Medicover Hospitals Hyderabad India.

⁶Assistance Publique-Hôpitaux de Paris, Unit of Clinical Epidemiology, Robert Debré children's hospital University of Paris Inserm U1123 and CIC-EC Paris France.

⁷AP-HP, European Georges Pompidou Hospital, Cardiology Department Paris France.

PubMed PMC DOI PDF

Abstract

Background Holter analysis requires significant clinical resources to achieve a high-quality diagnosis. This study sought to assess whether an artificial intelligence (AI)-based Holter analysis platform using deep neural networks is noninferior to a conventional one used in clinical routine in detecting a major rhythm abnormality. Methods and Results A total of 1000 Holter (24-hour) recordings wer…

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