TrackAF: Rethinking atrial fibrillation across a lifetime

Q: What is the clinical evidence from this study?

Study design: Editorial. Population: Atrial fibrillation and atrial cardiomyopathy. Intervention: TrackAF doctoral network (multiscale research, physiology-informed AI, and atrial digital twins).

Q: What does this research mean for the field?

Integrating multiscale cardiac modeling, experimental biology, and interpretable AI across a patient's lifespan is essential to achieve personalized prevention, stratification, and treatment for atrial fibrillation and atrial cardiomyopathy. Novelty: ClaimNovelty.METHODOLOGICAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Axel Loewe

doi:10.56367/oag-052-12808

October 14, 2026Open Access GovernmentOpen Access

TrackAF: Rethinking atrial fibrillation across a lifetime

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Key result

The TrackAF European doctoral network will train 15 doctoral candidates to integrate cardiac biology, clinical data, and interpretable AI for personalized atrial fibrillation management.

Key Points

TrackAF aims to understand atrial fibrillation and atrial cardiomyopathy across a patient's entire lifetime by combining multiscale biological mechanisms, clinical datasets, and artificial intelligence.
Coordinates a European doctoral network training 15 doctoral candidates across cardiology, biomedical engineering, wet-lab research, data science, and AI.
Combines wearable sensor data, electrocardiograms, imaging, deep phenotyping, stem-cell models, and cardiac organoids to construct patient-specific computer models and atrial digital twins.
Applies interpretable AI, privacy-preserving federated learning, and in silico trials to simulate catheter ablation, autonomic modulation, and pharmacotherapies across virtual cohorts.
Constructs multiscale atrial digital twins to investigate patient-specific failure and success mechanisms for catheter ablation and drug treatments.
Integrates data-driven machine learning with biophysically grounded cardiac physiology models to enable interpretable, bias-checked clinical decision support.
Incorporates sex- and age-stratified analyses alongside lifestyle comorbidities, including hypertension, diabetes, obesity, and sleep apnoea, to model disease progression and regression.

Structured PICO

P

Population

Patients with or at risk for atrial fibrillation (AF) and atrial cardiomyopathy (AtCM) across Europe (descriptive overview of a research network; no clinical study sample enrolled).

I

Intervention

TrackAF (Translational Research and Training on Atrial Cardiomyopathy as a Key to Better Atrial Fibrillation Management) programme combining deep phenotyping, biomarkers, wearable sensors, machine learning, human stem-cell/organoid models, and patient-specific atrial digital twins.

Implication

Describes the European TrackAF doctoral network initiative aimed at combining experimental cardiac science, mechanistic digital twin modeling, and interpretable AI to personalize atrial fibrillation prevention, diagnosis, and management.

Abstract

TrackAF: Rethinking atrial fibrillation across a lifetime Axel Loewe from Karlsruhe Institute of Technology (KIT) shares how a European doctoral network is combining heart science, clinical data and AI to personalise care. Atrial fibrillation (AF) is the most common sustained heart rhythm disorder. Its irregular electrical activity can be intermittent or difficult to detect, yet its consequences can be profound: stroke, heart failure, cognitive decline, poorer quality of life, premature death and rising healthcare costs. With Europe’s population ageing, the need to prevent AF, recognise it earlier and select the right treatment is becoming urgent.

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Cite This Study

Axel Loewe (2026) conducted an editorial in Atrial fibrillation and atrial cardiomyopathy. TrackAF doctoral network (multiscale research, physiology-informed AI, and atrial digital twins) was evaluated. The TrackAF European doctoral network will train 15 doctoral candidates to integrate cardiac biology, clinical data, and interpretable AI for personalized atrial fibrillation management.

synapsesocial.com/papers/6adbf6895fff1ad8bae034b2 https://doi.org/10.56367/oag-052-12808