This paper is in the following e-collection/theme issue:

mHealth for Screening (264) Cardiac Arrhythmia, Atrial Fibrillation (107) Novel Sensors and Data Acquisition Methods in Cardiology (214)

Published on in Vol 7, No 3 (2019): March

Preprints (earlier versions) of this paper are available at https://preprints.jmir.org/preprint/12284, first published .
Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Mobile Phone–Based Use of the Photoplethysmography Technique to Detect Atrial Fibrillation in Primary Care: Diagnostic Accuracy Study of the FibriCheck App

Authors of this article:

Tine Proesmans1 Author Orcid Image ;   Christophe Mortelmans2 Author Orcid Image ;   Ruth Van Haelst2 Author Orcid Image ;   Frederik Verbrugge1 Author Orcid Image ;   Pieter Vandervoort1 Author Orcid Image ;   Bert Vaes2 Author Orcid Image
Article Authors Cited by (94) Tweetations (8) Metrics

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  82. Gruwez H, Van Herendael H, Maris S, Dhont S, Meekers E, Bruckers L, Wouters F, Rivero-Ayerza M, Nuyens D, Vandervoort P, Haemers P, Pison L. Effectiveness of photoplethysmography-based rhythm monitoring after atrial fibrillation ablation using a smartphone application: DIGITOTAL study. Heart Rhythm 2024 View
  83. Isaksen J, Arildsen B, Lind C, Nørregaard M, Vernooy K, Schotten U, Jespersen T, Betz K, Hermans A, Kanters J, Linz D. Raw photoplethysmogram waveforms versus peak-to-peak intervals for machine learning detection of atrial fibrillation: Does waveform matter?. Computer Methods and Programs in Biomedicine 2025;260:108537 View
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  85. Breen C, Hendriks J. Advancing cardiovascular health: photoplethysmography as a tool for electrocardiogram signal acquisition. European Journal of Cardiovascular Nursing 2025;24(2):314 View
  86. Francisco A, Pascoal C, Lamborne P, Morais H, Gonçalves M. Wearables and Atrial Fibrillation: Advances in Detection, Clinical Impact, Ethical Concerns, and Future Perspectives. Cureus 2025 View
  87. Barbosa I, de Oliveira B, Santos C, Miranda M, Barbosa G, Júnior A. Smartphone-Based Applications for Atrial Fibrillation Detection: A Systematic Review and Meta-Analysis of Diagnostic Test Accuracy. Telemedicine and e-Health 2025;31(6):687 View
  88. Fernstad J, Svennberg E, Åberg P, Kemp Gudmundsdottir K, Jansson A, Engdahl J. External validation of a machine learning-based classification algorithm for ambulatory heart rhythm diagnostics in pericardioversion atrial fibrillation patients using smartphone photoplethysmography: the SMARTBEATS-ALGO study. Europace 2025;27(4) View
  89. Basem J, Mani R, Sun S, Gilotra K, Dianati-Maleki N, Dashti R. Clinical applications of artificial intelligence and machine learning in neurocardiology: a comprehensive review. Frontiers in Cardiovascular Medicine 2025;12 View
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Books/Policy Documents

  1. Tao J, Xie S, Jiang J, Wei W. Artificial Intelligence Algorithms and Applications. View
  2. Lemay M, Bertschi M, Sola J, Renevey P, Genzoni E, Proença M, Ferrario D, Braun F, Parak J, Korhonen I. Wearable Sensors. View