Artificial intelligence for the detection, prediction, and management of atrial fibrillation

Research output: Contribution to journal › Review › Research › peer-review

Standard

Artificial intelligence for the detection, prediction, and management of atrial fibrillation. / Isaksen, Jonas L; Baumert, Mathias; Hermans, Astrid N L; Maleckar, Molly; Linz, Dominik.

In: Herzschrittmachertherapie und Elektrophysiologie, Vol. 33, 2022, p. 34–41.

Research output: Contribution to journal › Review › Research › peer-review

Harvard

Isaksen, JL, Baumert, M, Hermans, ANL, Maleckar, M & Linz, D 2022, 'Artificial intelligence for the detection, prediction, and management of atrial fibrillation', Herzschrittmachertherapie und Elektrophysiologie, vol. 33, pp. 34–41. https://doi.org/10.1007/s00399-022-00839-x

APA

Isaksen, J. L., Baumert, M., Hermans, A. N. L., Maleckar, M., & Linz, D. (2022). Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmachertherapie und Elektrophysiologie, 33, 34–41. https://doi.org/10.1007/s00399-022-00839-x

Vancouver

Isaksen JL, Baumert M, Hermans ANL, Maleckar M, Linz D. Artificial intelligence for the detection, prediction, and management of atrial fibrillation. Herzschrittmachertherapie und Elektrophysiologie. 2022;33:34–41. https://doi.org/10.1007/s00399-022-00839-x

Author

Isaksen, Jonas L ; Baumert, Mathias ; Hermans, Astrid N L ; Maleckar, Molly ; Linz, Dominik. / Artificial intelligence for the detection, prediction, and management of atrial fibrillation. In: Herzschrittmachertherapie und Elektrophysiologie. 2022 ; Vol. 33. pp. 34–41.

Bibtex

@article{380787f7079644e1a04a60cdd8c31b17,

title = "Artificial intelligence for the detection, prediction, and management of atrial fibrillation",

abstract = "The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.",

author = "Isaksen, {Jonas L} and Mathias Baumert and Hermans, {Astrid N L} and Molly Maleckar and Dominik Linz",

note = "{\textcopyright} 2022. The Author(s).",

year = "2022",

doi = "10.1007/s00399-022-00839-x",

language = "English",

volume = "33",

pages = "34–41",

journal = "Herzschrittmachertherapie und Elektrophysiologie",

issn = "0938-7412",

publisher = "D. Steinkopff-Verlag",

}

RIS

TY - JOUR

T1 - Artificial intelligence for the detection, prediction, and management of atrial fibrillation

AU - Isaksen, Jonas L

AU - Baumert, Mathias

AU - Hermans, Astrid N L

AU - Maleckar, Molly

AU - Linz, Dominik

PY - 2022

Y1 - 2022

N2 - The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.

AB - The present article reviews the state of the art of machine learning algorithms for the detection, prediction, and management of atrial fibrillation (AF), as well as of the development and evaluation of artificial intelligence (AI) in cardiology and beyond. Today, AI detects AF with a high accuracy using 12-lead or single-lead electrocardiograms or photoplethysmography. The prediction of paroxysmal or future AF currently operates at a level of precision that is too low for clinical use. Further studies are needed to determine whether patient selection for interventions may be possible with machine learning.

U2 - 10.1007/s00399-022-00839-x

DO - 10.1007/s00399-022-00839-x

M3 - Review

C2 - 35147766

VL - 33

SP - 34

EP - 41

JO - Herzschrittmachertherapie und Elektrophysiologie

JF - Herzschrittmachertherapie und Elektrophysiologie

SN - 0938-7412

ER -

ID: 291986873