Reducing the burden of inconclusive smart device single-lead ECG tracings via a novel artificial intelligence algorithm

Weidlich, Simon; Mannhart, Diego; Kennedy, Alan; Doggart, Peter; Serban, Teodor; Knecht, Sven; Du Fay de Lavallaz, Jeanne; Kühne, Michael; Sticherling, Christian; Badertscher, Patrick

doi:10.1016/j.cvdhj.2023.12.003

Cited by 1 publication

(1 citation statement)

References 23 publications

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“…Avram et al demonstrated that obtaining a confirmatory iECG one hour apart the first one improved the specificity (100.0%) without affecting sensitivity (96.0%). Moreover, novel AI algorithms have demonstrated excellent performance in reducing inconclusive classification rates ( 47 ). Lastly, in most studies, patients were instructed on how to use the technology for AF detection which may not reflect real-life circumstances or digital literacy, especially among the elderly.…”

Section: Introductionmentioning

confidence: 99%

Artificial intelligence-enabled atrial fibrillation detection using smartwatches: current status and future perspectives

Papalamprakopoulou,

Stavropoulos,

Moustakidis

et al. 2024

Front. Cardiovasc. Med.

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Atrial fibrillation (AF) significantly increases the risk of stroke and heart failure, but is frequently asymptomatic and intermittent; therefore, its timely diagnosis poses challenges. Early detection in selected patients may aid in stroke prevention and mitigate structural heart complications through prompt intervention. Smartwatches, coupled with powerful artificial intelligence (AI)-enabled algorithms, offer a promising tool for early detection due to their widespread use, easiness of use, and potential cost-effectiveness. Commercially available smartwatches have gained clearance from the FDA to detect AF and are becoming increasingly popular. Despite their promise, the evolving landscape of AI-enabled smartwatch-based AF detection raises questions about the clinical value of this technology. Following the ongoing digital transformation of healthcare, clinicians should familiarize themselves with how AI-enabled smartwatches function in AF detection and navigate their role in clinical settings to deliver optimal patient care. In this review, we provide a concise overview of the characteristics of AI-enabled smartwatch algorithms, their diagnostic performance, clinical value, limitations, and discuss future perspectives in AF diagnosis.

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Section: Introductionmentioning

confidence: 99%