Prospective blinded Evaluation of the smartphone-based AliveCor Kardia ECG monitor for Atrial Fibrillation detection: The PEAK-AF study

Wegner, Felix K.; Kochhäuser, Simon; Ellermann, Christian; Lange, Philipp; Frommeyer, Gerrit; Leitz, Patrick; Eckardt, Lars; Dechering, Dirk G.

doi:10.1016/j.ejim.2019.11.018

Cited by 61 publications

(66 citation statements)

References 9 publications

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“…As AF is a risk factor for stroke, early detection may be important to prompt adequate anticoagulant treatment. [67][68][69] An irregular rhythm can be accurately detected using smartphone or smartwatch-acquired ECGs. Even predicting whether a patient will develop AF in the future using smartphone-acquired ECGs recorded during sinus rhythm has been recently reported.…”

Section: Opportunities For Artificial Intelligence In Electrophysiolomentioning

confidence: 99%

“…Even predicting whether a patient will develop AF in the future using smartphone-acquired ECGs recorded during sinus rhythm has been recently reported. 69,70 Also, camera-based photoplethysmography recordings can be used to differentiate between irregular and regular cardiac rhythm. 71,72 However, under-detection of asymptomatic AF is expected as the use of applications requires active use and people are likely to only use applications when they have a health complaint.…”

Section: Opportunities For Artificial Intelligence In Electrophysiolomentioning

confidence: 99%

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Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Leur

Boonstra

Bagheri

et al. 2020

Arrhythm Electrophysiol Rev

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The combination of big data and artificial intelligence (AI) is having an increasing impact on the field of electrophysiology. Algorithms are created to improve the automated diagnosis of clinical ECGs or ambulatory rhythm devices. Furthermore, the use of AI during invasive electrophysiological studies or combining several diagnostic modalities into AI algorithms to aid diagnostics are being investigated. However, the clinical performance and applicability of created algorithms are yet unknown. In this narrative review, opportunities and threats of AI in the field of electrophysiology are described, mainly focusing on ECGs. Current opportunities are discussed with their potential clinical benefits as well as the challenges. Challenges in data acquisition, model performance, (external) validity, clinical implementation, algorithm interpretation as well as the ethical aspects of AI research are discussed. This article aims to guide clinicians in the evaluation of new AI applications for electrophysiology before their clinical implementation.

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Section: Opportunities For Artificial Intelligence In Electrophysiolomentioning

confidence: 99%

Section: Opportunities For Artificial Intelligence In Electrophysiolomentioning

confidence: 99%

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Leur

Boonstra

Bagheri

et al. 2020

Arrhythm Electrophysiol Rev

View full text Add to dashboard Cite

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“…serung der Detektion von Herzrhythmusstörungen ermöglicht [15][16][17] [19]. Die aktuell noch laufende MOBILE-AF-Studie evaluiert den diagnostischen Stellenwert des Kardia EKG Monitor gegenüber einem implantierten Ereignisrekorder hinsichtlich der Detektion von Vorhofflimmern bei Patienten mit kryptogenem Schlaganfall [20].…”

Section: Smart and Simple Current Role Of Implantables And Wearablesunclassified

“…Im Rahmen der aktuell bestehenden COVID-19-Krise wurde zudem eine QTc-Messmethode des 6‑Kanal-EKGs geprüft und ebenfalls zugelassen. Eine aktuelle prospektive Studie zur Verwendung des Kardia EKG Monitors bei 99 Patienten hat gezeigt, dass symptomatische Episoden von Vorhofflimmern mit hoher diagnostischer Qualität erfasst werden können [ 19 ]. Daher sehen die Autoren ein geeignetes Einsatzgebiet des 1‑Kanal EKG-Monitors beim Screening von Patienten mit Vorhofflimmern.…”

Section: Wearablesunclassified

“…Daher sehen die Autoren ein geeignetes Einsatzgebiet des 1‑Kanal EKG-Monitors beim Screening von Patienten mit Vorhofflimmern. Bei Patienten mit atrialen Tachykardien oder Trägern von Herzschrittmachern fand man noch technische Limitationen [ 19 ]. Die aktuell noch laufende MOBILE-AF-Studie evaluiert den diagnostischen Stellenwert des Kardia EKG Monitor gegenüber einem implantierten Ereignisrekorder hinsichtlich der Detektion von Vorhofflimmern bei Patienten mit kryptogenem Schlaganfall [ 20 ].…”

Section: Wearablesunclassified

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Smart and simple

Sohns

Khalaph

Bergau

et al. 2020

Herzschr Elektrophys

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Smart and simple Stellenwert von Implantables und Wearables im rhythmologischen Alltag Im Rahmen der Digitalisierung besteht die Möglichkeit, Herzrhythmusstörungen in einem relevanten Umfang über sog. Implantables und Wearables zu detektieren. Diese Geräte produzieren eine erhebliche Datenmenge und geben einen Einblick in das, was in unserem Körper vor sich geht. Es ist nicht überraschend, dass diese Geräte zunehmend an Popularität gewinnen, da viele Menschen nun die direkte Möglichkeit haben, individuelle Veränderungen, Fortschritte, aber auch Rückschritte kontinuierlich und quantitativ zu erfassen und darauf zu reagieren.

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Prevalence of atrial fibrillation based on tertiary hospital survey in Indonesia: A smartphone‐based diagnosis

Yuniadi,

Supit,

Hanafy

et al. 2024

Journal of Arrhythmia

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ObjectiveAtrial fibrillation (AF) is one of the most common arrhythmic disorders worldwide. This study aims to describe the prevalence of AF in various cities in Indonesia using single‐lead hand held electrocardiography linked to a smartphone‐based application.MethodsThis is a cross‐sectional epidemiological study conducted at tertiary hospital of major cities in Indonesia, between January 2018 and July 2019. The AliveCor Kardia™ Mobile system device was used as a screening tool for AF and confirmed its finding with clinical diagnoses made by cardiologists.ResultsA total of 9773 subjects were enrolled in this study. The prevalence of AF reported by the device and cardiologist was 3.2% and 3.5%, respectively. AF prevalences are equal in men than women. The majority of subjects (56%) are having low risk for stroke or systemic thromboembolism. Prevalence of risk factors such as heart failure, hypertension, diabetes, and history of stroke among AF patients were 50%, 22.9%, 31.9%, 13.5%, and 5.8%, respectively.ConclusionThe prevalence of AF at referral hospitals visitors based on smartphone diagnosis in Indonesia is 3.2%.

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Prospective blinded Evaluation of the smartphone-based AliveCor Kardia ECG monitor for Atrial Fibrillation detection: The PEAK-AF study

Cited by 61 publications

References 9 publications

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Big Data and Artificial Intelligence: Opportunities and Threats in Electrophysiology

Smart and simple

Prevalence of atrial fibrillation based on tertiary hospital survey in Indonesia: A smartphone‐based diagnosis

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