journal article·2026-06Open access

3D‐Printed Dynamic Heart Model With Left‐Side Anatomy and Integrated Sensor for Edge‐to‐Edge Repair and Regurgitation Reduction

Alejandro Guillen Obando, Hongyi Shen, Myles McGovern, Yusen Zhang, Vivien Lin, Darryl Fu, Ryan Baumwart, Kaiyan Qiu

Advanced Materials Technologies· Volume 11· Issue 11

Abstract

ABSTRACT Heart disease remains a major cause of morbidity and mortality in the United States, accounting for roughly 20% of all deaths. Minimally invasive procedures have been used to treat cardiovascular diseases; however, the heart's anatomical complexity and dynamics require proper hands‐on training on patient‐specific presurgical models to reduce procedural errors. Existing dynamic heart models often rely on animal cardiac tissues to support pumping mechanics, introducing ethical and policy concerns. Fully synthetic heart models offer an alternative by using actuation devices to reproduce cardiac circulation and contraction, yet accurately replicating the full dynamics of the heart remains challenging. This research presents a 3D‐printed dynamic heart model representing left‐side anatomy (atrium, ventricle, and mitral valve) to support minimally invasive procedures. Soft material 3D‐printing is employed to replicate key anatomical features. Sutures anchor the ventricle to the mitral valve, replicating chordae tendineae‐like structures, adding physiological realism and structural complexity. McKibben actuators are embedded within the myocardial walls, mimicking ventricular contraction and realistic mitral valve motion. Customized flexible pressure sensors are designed and incorporated to monitor pressure changes inside the model. These novel features enable the model to function as a platform for hemodynamic studies and simulation of edge‐to‐edge repair to mitigate atrioventricular valve regurgitation.
Source: crossref · Quality: baseline

Research context

OpenAlex topics: Soft Robotics and Applications · Cardiac Valve Diseases and Treatments · Elasticity and Material Modeling

SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.

Affiliations

School of Mechanical and Materials Engineering Washington State University Pullman Washington USA

College of Veterinary Medicine Washington State University Pullman Washington USA

Funding

National Science Foundation

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