Key result
Assert-IQ ICM AI algorithm cuts AF detection false positives by ~73% to improve PPV.
Why the study?
Accurate AF detection and burden assessment are critical for insertable cardiac monitors to guide treatments, but false-positive episodes remain a challenge.
Does the Assert-IQ insertable cardiac monitor with an AI algorithm accurately detect atrial fibrillation and reduce false positives in patients with symptomatic, drug-refractory AF?
Observational (n=151)
Yes
Does the Assert-IQ insertable cardiac monitor with an AI algorithm accurately detect atrial fibrillation and reduce false positives in patients with symptomatic, drug-refractory AF?
The Assert-IQ insertable cardiac monitor combined with a novel AI algorithm demonstrates high accuracy for AF detection and significantly reduces false-positive episodes.
Background: Accurate atrial fibrillation (AF) detection and burden assessment are critical features of modern insertable cardiac monitors (ICMs), enabling precise determination of AF episode patterns, frequency, duration, and total burden to guide treatments. Objective: This study aimed to evaluate the AF detection performance of the Assert-IQ ICM and assess the impact of an artificial intelligence (AI) algorithm designed for reducing false-positive AF episodes. Methods: This prospective, single-arm, multicenter study enrolled 151 subjects with symptomatic, drug-refractory paroxysmal or persistent AF. A Holter assessment was conducted after ICM insertion. AF detection metrics-sensitivity, specificity, positive predictive value (PPV), and negative predictive value-were evaluated by comparing ICM detections with core laboratory-annotated Holter AF events. The impact of an AI algorithm on AF detection performance was then assessed. Results: Among 135 analyzable patients, 39 had Holter-confirmed AF with 522 episodes lasting ≥2 minutes. Assert-IQ ICM correctly identified all patients with true AF. Duration-based sensitivity, specificity, PPV, negative predictive value, and accuracy were 93.0%, 99.3%, 97.4%, 98.0%, and 97.9%, respectively. Episode detection sensitivity was 99.4% (gross) and 99.9% (patient average). AF burden correlation between ICM and Holter was excellent (r = 0.99). The AI algorithm retained all true positives and reduced 72.6% of false positives, improving PPV from 79.9% to 93.6%. Conclusion: Assert-IQ ICM accurately detects AF and quantifies burden for long-term monitoring. The AI algorithm effectively reduces false positives while maintaining high sensitivity.
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Birgersdotter‐Green et al. (2025) conducted an observational in Atrial fibrillation (n=151). Assert-IQ insertable cardiac monitor and AI algorithm vs. Holter monitor was evaluated on Atrial fibrillation detection metrics including sensitivity, specificity, positive predictive value, and negative predictive value. The Assert-IQ ICM demonstrated 93.0% duration-based sensitivity and 97.9% accuracy for AF detection, and an AI algorithm reduced false positives by 72.6%, improving PPV from 79.9% to 93.6%.
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