Aortic stenosis (AS) affects about 1.5 million people in the United States and is associated with a 5-year survival rate of 20% if untreated. In these patients, aortic valve replacement is performed to restore adequate hemodynamics and alleviate symptoms. The development of next-generation prosthetic aortic valves seeks to provide enhanced hemodynamic performance, durability, and long-term safety, emphasizing the need for high-fidelity testing platforms for these devices. We propose a soft robotic model that recapitulates patient-specific hemodynamics of AS and secondary ventricular remodeling which we validated against clinical data. The model leverages 3D-printed replicas of each patient’s cardiac anatomy and patient-specific soft robotic sleeves to recreate the patients’ hemodynamics. An aortic sleeve allows mimicry of AS lesions due to degenerative or congenital disease, whereas a left ventricular sleeve recapitulates loss of ventricular compliance and diastolic dysfunction (DD) associated with AS. Through a combination of echocardiographic and catheterization techniques, this system is shown to recreate clinical metrics of AS with greater controllability compared with methods based on image-guided aortic root reconstruction and parameters of cardiac function that rigid systems fail to mimic physiologically. Last, we leverage this model to evaluate the hemodynamic benefit of transcatheter aortic valves in a subset of patients with diverse anatomies, etiologies, and disease states. Through the development of a high-fidelity model of AS and DD, this work demonstrates the use of soft robotics to recreate cardiovascular disease, with potential applications in device development, procedural planning, and outcome prediction in industrial and clinical settings.

中文翻译：

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主动脉瓣狭窄和心室重塑的软机器人患者特定流体动力学模型

主动脉瓣狭窄 (AS) 在美国影响约 150 万人，如果不治疗，5 年生存率为 20%。在这些患者中，进行主动脉瓣置换术以恢复足够的血流动力学并缓解症状。下一代人工主动脉瓣的开发旨在提供增强的血流动力学性能、耐用性和长期安全性，强调这些设备对高保真测试平台的需求。我们提出了一种软机器人模型，可以概括患者特定的 AS 血流动力学和继发性心室重塑，并根据临床数据进行验证。该模型利用每位患者心脏解剖结构的 3D 打印复制品和患者专用的软机器人套管来重建患者的血流动力学。主动脉袖可以模仿由退行性或先天性疾病引起的 AS 病变，而左心室袖则再现与 AS 相关的心室顺应性丧失和舒张功能障碍 (DD)。通过结合超声心动图和导管技术，该系统能够重建 AS 的临床指标，与基于图像引导主动脉根部重建和刚性系统无法模拟生理学的心脏功能参数的方法相比，具有更大的可控性。最后，我们利用该模型来评估经导管主动脉瓣对具有不同解剖结构、病因和疾病状态的患者子集的血流动力学益处。通过开发 AS 和 DD 的高保真模型，这项工作展示了使用软机器人技术来重现心血管疾病，