Evidence map›Paper›PMID 41455001›Full record

Trial reportAdvances in therapy2026

Artificial Intelligence Software for Detecting Paroxysmal Atrial Fibrillation from Sinus Rhythm Monitor ECG: Development and Clinical Trial.

Yuichi Tamura, Tomohiro Takata, Hirohisa Taniguchi, Ryo Takemura, Mineki Takechi, Rika Takeyasu, Eiichi Watanabe, Hirotaka Yada, Yudai Tamura, Jin Iwasawa and 2 more

Abstract readMulticenter StudyClinical Trial
PubMed Publisher
In one paragraph

Trial report in Advances in therapy, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

12 authors.

Yuichi TamuraDepartment of Cardiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan. tamura.u1@gmail.com.ORCID 0000-0002-4437-8019
Tomohiro TakataCardio Intelligence Inc., Minato-ku, Tokyo, Japan.
Hirohisa TaniguchiDepartment of Cardiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan.
Ryo TakemuraClinical and Translational Research Center, Keio University Hospital, Shinjuku-ku, Tokyo, Japan.
Mineki TakechiCardio Intelligence Inc., Minato-ku, Tokyo, Japan.
Rika TakeyasuPulmonary Hypertension Center, International University of Health and Welfare Mita Hospital, Minato-ku, Tokyo, Japan.
Eiichi WatanabeDepartment of Cardiology, Fujita Health University Bantane Hospital, Nagoya, Aichi, Japan.
Hirotaka YadaDepartment of Cardiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan.
Yudai TamuraDepartment of Cardiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan.
Jin IwasawaDepartment of Cardiology, International University of Health and Welfare Mita Hospital, 1-4-3 Mita, Minato-ku, Tokyo, 108-8329, Japan.
Tadahiro TaniguchiResearch Organization of Science and Technology, Ritsumeikan University, Shiga, Japan.
Satoshi OgawaOgawa Satoshi Clinic, Minato-ku, Tokyo, Japan.

Funding

Japan Agency for Medical Research and Development 21hk0102071h0003
6 · The paper itself

Abstract

introductionDetecting paroxysmal atrial fibrillation (pAF) from sinus rhythm could enable earlier intervention and stroke prevention. We developed a deep-learning Holter electrocardiograph (ECG) algorithm and prospectively evaluated its patient-level performance against 7-day AF outcomes.

methodsWe curated 20,000 30-s sinus rhythm blocks (125 Hz) from Holter ECG data of patients with and without pAF, trained convolutional models with tenfold cross-validation, and assessed a separate validation set (n = 54; 27 pAF, 27 controls) to select the operating threshold. A multicenter prospective study then evaluated the algorithm using ten consecutive 30-s sinus rhythm blocks per patient with a 4/10 positive rule; patients with pAF underwent concurrent 7-day patch monitoring to anchor outcomes.

resultsCross-validation during development yielded mean sensitivity 84.2% and specificity 66.2%; the best tuned model achieved 84.9% sensitivity and 69.9% specificity on the separate set. In the clinical trial, among 24 patients with AF documented within 7 days and 20 controls, the device showed sensitivity 91.7% (95% confidence interval (CI) 73.0-99.0) and specificity 65.0% (40.8-84.6). No device-related adverse events occurred.

conclusionAn artificial intelligence (AI) analyzing short sinus rhythm Holter segments can identify patients who develop pAF within 7 days, supporting use as a triage tool for intensified rhythm monitoring.

trial registrationUMIN-CTR UMIN000047182.

Indexed as

Artificial IntelligenceAtrial FibrillationDeep LearningElectrocardiography, AmbulatorySoftwareAgedAlgorithmsFemaleHumansMaleMiddle AgedProspective StudiesSensitivity and SpecificityArtificial intelligenceAtrial fibrillation screeningConvolutional neural networkDeep learningElectrocardiographyHolter monitoringMedical device softwareParoxysmal atrial fibrillation

Identifiers

PMID41455001

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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.