Integrating soft robotics and computational models to study left atrial hemodynamics and device testing in sinus rhythm and atrial fibrillation

Atrial fibrillation (AF) poses significant clinical challenges due to the complex and variable geometry of the left atrial appendage (LAA), whose structure complicates the development of personalized interventions like LAA occlusion (LAAO) for stroke prevention Current reliance on animal models and cadavers for the assessment of left atrium (LA) and LAA to study AF-related disease and interventions raises reproducibility concerns, necessitating the development of high fidelity, physiologically relevant tools. To address this, we present a multimodal framework combining a soft robotic benchtop simulator, a lumped parameter model (LPM), and finite element analysis (FEA) to replicate LA function in sinus rhythm, atrial flutter, and AF. The system integrates 3D-printed, patient-specific LA geometries with soft robotic actuators to reproduce realistic wall motion and hemodynamics. A compact, magnetic resonance imaging (MRI)-compatible flow loop, driven by a soft robotic left ventricle (LV), eliminates bulky and non-physiological pulsatile pumps, allowing precise flow measurements and LAAO device testing under clinically relevant conditions. Complementary LPM and FEA models provide mechanistic insights, quantifying systemic hemodynamic changes and LA wall stress during disease and interventions. The models effectively replicate the clinical markers of atrial dysfunction and arrhythmia disorders, and their physiological accuracy is demonstrated through validation against human imaging and porcine models. The soft robotic LV’s ability to drive the mock flow loop is validated in a hybrid synthetic-biological configuration within a swine circulatory system, where the soft robotic ventricle replaces native ventricular contraction to sustain systemic circulation. This scalable and versatile framework integrates experimental and computational techniques to advance cardiovascular biomechanics, supporting device development, clinical research/training, and personalized AF treatment to improve patient outcomes.