Using virtual twin-based AI models to detect atrial fibrillation and improve stroke outcomes [TAILOR]: a multicentre prospective cohort study

van Kempen, EJ ORCID: 0009-0009-1608-8063, Jiménez-Balado, J, Jiménez-Conde, J, Meijer, FJA ORCID: 0000-0001-5921-639X, Ten Cate, TJ, Palacio-Gili, A ORCID: 0009-0006-1796-6479, Ortega-Martorell, S ORCID: 0000-0001-9927-3209, Beltran Marmol, B, Tuladhar, A, Giralt-Steinhauer, E ORCID: 0000-0002-7463-0983 and TARGET Consortium, NA (2025) Using virtual twin-based AI models to detect atrial fibrillation and improve stroke outcomes [TAILOR]: a multicentre prospective cohort study. BMJ open, 15 (12). e106518. ISSN 2044-6055

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Abstract

Introduction: Atrial fibrillation (AF) is the leading cause of cardioembolic stroke and is associated with increased stroke severity and fatality. Early identification of AF is essential for adequate secondary prevention but remains challenging due to its often asymptomatic or paroxysmal occurrence. Artificial intelligence (AI) offers new possibilities by integrating biomarkers, clinical phenotypes, established risk factors and imaging features to define a personalised ‘digital twin’ model. The TAILOR study aims to (1) examine prospective detection of AF using monitoring devices, (2) investigate novel prognostic MRI markers in patients with an AF-related stroke (AFRS) and (3) validate AI-based models for outcome prediction in AFRS.

Methods and analysis: This prospective multicentre observational cohort study includes patients aged 40 years and above, with neuroimaging-confirmed diagnosis of ischaemic stroke, recruited from two sites: Hospital del Mar Barcelona (Spain) and Radboud University Medical Centre (The Netherlands). For the first sub-study (n=300), patients will undergo clinical assessment at baseline, 3 months and 12 months, and patch-based or Holter cardiac monitoring. The second sub-study (n=200) involves repeated brain MRI and cognitive examination after AFRS. Finally, AI-driven ‘digital twin’ models developed on retrospective TARGET datasets will be prospectively evaluated in TAILOR using temporal and centre-stratified analyses for advanced predictive tools for AF and AFRS outcomes.

Ethics and dissemination: The TAILOR study was approved by local ethics boards in Barcelona (CPMP/ICH/135/95) and Medical Research Ethics Committee Oost-Nederland (NL86346.091.24). Patients will be included after providing informed consent. Study results will be presented in peer-reviewed journals and at global conferences.

Item Type:	Article
Uncontrolled Keywords:	TARGET Consortium; Humans; Atrial Fibrillation; Magnetic Resonance Imaging; Prognosis; Risk Factors; Prospective Studies; Artificial Intelligence; Adult; Middle Aged; Netherlands; Female; Male; Multicenter Studies as Topic; Stroke; Observational Studies as Topic; Ischemic Stroke; Adult cardiology; Clinical Trial; Stroke; Humans; Atrial Fibrillation; Prospective Studies; Magnetic Resonance Imaging; Artificial Intelligence; Stroke; Female; Male; Prognosis; Middle Aged; Adult; Risk Factors; Multicenter Studies as Topic; Netherlands; Observational Studies as Topic; Ischemic Stroke; 32 Biomedical and Clinical Sciences; 3202 Clinical Sciences; Cerebrovascular; Heart Disease; Biomedical Imaging; Neurosciences; Networking and Information Technology R&D (NITRD); Machine Learning and Artificial Intelligence; Cardiovascular; Prevention; Clinical Research; Stroke; Brain Disorders; 4.2 Evaluation of markers and technologies; 4.1 Discovery and preclinical testing of markers and technologies; Stroke; Cardiovascular; 3 Good Health and Well Being; Humans; Atrial Fibrillation; Prospective Studies; Magnetic Resonance Imaging; Artificial Intelligence; Stroke; Female; Male; Prognosis; Middle Aged; Adult; Risk Factors; Multicenter Studies as Topic; Netherlands; Observational Studies as Topic; Ischemic Stroke; 1103 Clinical Sciences; 1117 Public Health and Health Services; 1199 Other Medical and Health Sciences; 32 Biomedical and clinical sciences; 42 Health sciences; 52 Psychology
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science R Medicine > R Medicine (General)
Divisions:	Computer Science and Mathematics
Publisher:	BMJ
Date of acceptance:	16 November 2025
Date of first compliant Open Access:	9 January 2026
Date Deposited:	09 Jan 2026 11:04
Last Modified:	09 Jan 2026 11:04
DOI or ID number:	10.1136/bmjopen-2025-106518
URI:	https://researchonline.ljmu.ac.uk/id/eprint/27863

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