Accounting for inconclusive results and repeated testing: A framework for evaluating wearable electrocardiogram diagnostic performance with application to Apple Watch and artificial intelligence-enhanced interpretation.

Peter Doggart¹, Caitlin Fisher², Pardis Biglarbeigi³, Raymond Bond⁴, Dewar Finlay⁴, David Burke⁵, David McEneaney⁶, Maysah Salman⁷, Daniel Guldenring⁸, Alan Kennedy²

¹PulseAI Ltd, Belfast, United Kingdom; Ulster University, Belfast, United Kingdom. Electronic address: peter.doggart@pulseai.io.

²PulseAI Ltd, Belfast, United Kingdom.

³PulseAI Ltd, Belfast, United Kingdom; Department of Pharmacology and Therapeutics, University of Liverpool, United Kingdom.

⁴Ulster University, Belfast, United Kingdom.

⁵Beacon Hospital, Dublin, Ireland.

⁶Southern Health and Social Care Trust, Craigavon Area Hospital, Craigavon, United Kingdom.

⁷Belfast Health And Social Care Trust, Belfast, United Kingdom.

⁸HS Kempten, Kempten, Germany.

PubMed DOI

Abstract

BACKGROUND: Conventional wearable electrocardiogram (ECG) validation excludes inconclusive results and assumes single-attempt testing, which inflates reported diagnostic performance for atrial fibrillation (AF) detection. Current reporting frameworks do not reflect real-world clinical use. OBJECTIVE: The study aimed to introduce and evaluate an intention-to-diagnose (ITD) framework incorporating…

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