Patient‐specific in vitro testing for evaluating TAVR clinical performance—A complementary approach to current ISO standard testing

Kovarovic, Brandon; Rotman, Oren M.; Parikh, Puja B.; Slepian, Marvin J.; Bluestein, Danny

doi:10.1111/aor.13841

Cited by 8 publications

(14 citation statements)

References 25 publications

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“…While standardized flow-loops and fatigue testers have traditionally been used for many years, there is—similar as we intend with our development—an increasing trend towards more patient-specific testing. 9 …”

Section: Discussionmentioning

confidence: 99%

“…While standardized flow-loops and fatigue testers have traditionally been used for many years, there is-similar as we intend with our development-an increasing trend towards more patient-specific testing. 9 Besides device testing, the platform may be used for training purposes of cardiac surgeons. Beating heart surgery requires significant procedural expertise and training simulators for off-pump surgery have been proposed.…”

Section: Implications For Biomedical Device Testing Training and Animal Welfarementioning

confidence: 99%

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A Robot Mimicking Heart Motions: An Ex-Vivo Test Approach for Cardiac Devices

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Pfenniger

Omari

et al. 2021

Cardiovasc Eng Tech

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Purpose The pre-clinical testing of cardiovascular implants gains increasing attention due to the complexity of novel implants and new medical device regulations. It often relies on large animal experiments that are afflicted with ethical and methodical challenges. Thus, a method for simulating physiological heart motions is desired but lacking so far. Methods We developed a robotic platform that allows simulating the trajectory of any point of the heart (one at a time) in six degrees of freedom. It uses heart motion trajectories acquired from cardiac magnetic resonance imaging or accelero-meter data. The rotations of the six motors are calculated based on the input trajectory. A closed-loop controller drives the platform and a graphical user interface monitors the functioning and accuracy of the robot using encoder data. Results The robotic platform can mimic physiological heart motions from large animals and humans. It offers a spherical work envelope with a radius of 29 mm, maximum acceleration of 20 m/s2 and maximum deflection of ±19° along all axes. The absolute mean positioning error in x-, y- and z-direction is 0.21 ±0.06, 0.31 ±0.11 and 0.17 ±0.12 mm, respectively. The absolute mean orientation error around x-, y- and z-axis (roll, pitch and yaw) is 0.24 ±0.18°, 0.23 ±0.13° and 0.18 ±0.18°, respectively. Conclusion The novel robotic approach allows reproducing heart motions with high accuracy and repeatability. This may benefit the device development process and allows re-using previously acquired heart motion data repeatedly, thus avoiding animal trials.

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Section: Discussionmentioning

confidence: 99%

Section: Implications For Biomedical Device Testing Training and Animal Welfarementioning

confidence: 99%

A Robot Mimicking Heart Motions: An Ex-Vivo Test Approach for Cardiac Devices

Zurbuchen

Pfenniger

Omari

et al. 2021

Cardiovasc Eng Tech

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“…(CT) data with a rigid pulse duplicator system (27). Similarly, Haghiashtiani et al combined image-guided aortic root models with a rigid pulsatile pumping system (28).…”

Section: Introductionmentioning

confidence: 99%

“…Recently, hydrodynamic platforms that integrate patient-specific aortic replicas have been developed for studies of congenital heart disease (23) , aortic dissection (24, 25) , and AS (26, 27) . Specifically, Kovarovic et al .…”

Section: Introductionmentioning

confidence: 99%

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Tunable, soft robotics-enabled patient-specific hydrodynamic model of aortic stenosis and secondary ventricular remodeling

Rosalia

Ozturk

Goswami

et al. 2022

Preprint

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Aortic stenosis (AS) affects approximately 1.5 million people in the US 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 of high-fidelity testing platforms for these devices. We propose a soft robotic model of AS capable of recapitulating patient-specific hemodynamics of AS and secondary ventricular remodeling, 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, while a left ventricular sleeve recapitulates loss of ventricular compliance, and impaired filling 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 to methods based on image-guided aortic root reconstruction, and parameters of cardiac function which rigid systems fail to mimic physiologically. Finally, we demonstrate the use of this model for the evaluation 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 secondary remodeling, this work pioneers the use of patient-specific soft robotic platforms of cardiovascular disease, with potential application in device development, procedural planning, and outcome prediction in industrial and clinical settings.One Sentence SummaryA high-fidelity, soft robotics-driven model recreates patient-specific biomechanics and hemodynamics of cardiovascular disease.

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AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis

Ozturk,

Pak,

Rosalia

et al. 2024

Advanced Science

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Aortic stenosis (AS) is the most common valvular heart disease in developed countries. High‐fidelity preclinical models can improve AS management by enabling therapeutic innovation, early diagnosis, and tailored treatment planning. However, their use is currently limited by complex workflows necessitating lengthy expert‐driven manual operations. Here, we propose an AI‐powered computational framework for accelerated and democratized patient‐specific modeling of AS hemodynamics from computed tomography (CT). First, we demonstrate that the automated meshing algorithms can generate task‐ready geometries for both computational and benchtop simulations with higher accuracy and 100 times faster than existing approaches. Then, we show that the approach can be integrated with fluid‐structure interaction and soft robotics models to accurately recapitulate a broad spectrum of clinical hemodynamic measurements of diverse AS patients. The efficiency and reliability of these algorithms make them an ideal complementary tool for personalized high‐fidelity modeling of AS biomechanics, hemodynamics, and treatment planning.

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Patient‐specific in vitro testing for evaluating TAVR clinical performance—A complementary approach to current ISO standard testing

Cited by 8 publications

References 25 publications

A Robot Mimicking Heart Motions: An Ex-Vivo Test Approach for Cardiac Devices

A Robot Mimicking Heart Motions: An Ex-Vivo Test Approach for Cardiac Devices

Tunable, soft robotics-enabled patient-specific hydrodynamic model of aortic stenosis and secondary ventricular remodeling

AI‐Powered Multimodal Modeling of Personalized Hemodynamics in Aortic Stenosis

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