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Dr. Wolfgang GOETZ, Prima Medical Technologies
Patients with heart failure often have a stiffened ascending aorta that impedes longitudinal shortening of the heart. Artract is a non-active medical device that frees the stationary apex of the heart from its pericardial confinement to allow inverse longitudinal left ventricular shortening, thereby increasing the efficacy of the heart. Dassault Systèmes computational modeling was utilized to demonstrate the effect of a stiff ascending aorta on left ventricular myocardial function and to simulate the effect of freeing the heart’s apex. As we continue to develop our medical device, we intend to use computer simulation to further improve its design and optimize its performance.
Dr. Zachary DARBY, Johns Hopkins University
Mitral regurgitation (MR) is the most common valvular heart disease and is categorized as primary or secondary MR. Secondary mitral regurgitation (SMR) is characterized by geometric alterations of the ventricle and annulus rather than the leaflets or chordal structures directly and is an independent predictor of incident heart failure and mortality. Presently available surgical therapies for SMR include either replacement or repair. Repair has been favored over replacement because it has been found to have the benefits of lower operative mortality, improved ventricular function, and freedom from complications related to prosthetic valves. These problems have prompted the development of several procedures to correct SMR. Our laboratory has developed a novel translocation repair strategy which creates circumferential augmentation of the MV leaflets, provides a supranormal surface of coaptation, and relieves MV leaflet tethering all while retaining the inherent structure of the MV. The MV translocation procedure is performed by first excising the native MV at the annulus, leaving the valve leaflets and subvavlular apparatus intact, followed by insertion of a circumferential autologous pericardial patch. This results in an en bloc translocation of the native mitral valve into the LV. Translocation results in a supranormal surface of coaptation, relieves leaflet tethering and has shown to have excellent mid-term freedom from recurrent MR. The translocation patch geometry has undergone revisions based on ex vivo models, in vivo animal models, and clinical experience. However, these steps are lengthy, time intensive, and costly. We joined as a member of the Living Heart Project (LHP) because we believe that the use of a computational model will aid in development of optimized patch geometry and are excited to share the progress we have made in the first 6 months of working with the LHP’s Left Heart Model.
Dr. David HOGANSON, Boston Children’s Hospital
3D modeling has become a standard approach in the pre-operative planning of complex congenital heart disease. Although various approaches have been utilized over the past several years, our team has developed an all-digital workflow for patient specific 3D modeling. This includes clinical display for pre-operative planning and virtual surgery for complex reconstruction utilizing 3DExperience platform. Workflows are being developed for computational planning of reconstructions including arch reconstruction, Fontan reconstruction, intracardiac baffles and pulmonary artery reconstruction. Mechanical properties of patches and native cardiac tissue are centrally important in the anatomic and physiologic outcome of these reconstructions. These mechanical properties are being integrated into the reconstruction planning calculations. Computational fluid dynamics simulation has become another centrally important workflow to aid in decision making in complex single ventricle patients. Different operative strategies can be analyzed and evaluated to plan the most efficient and physiologically normal reconstructions. These engineering workflows for computational pre-operative planning are accomplished by a team of engineers with expertise in CAD, modeling, simulation and the anatomic and physiologic complexities of congenital heart disease who work in concert with the patients cardiac surgeon and cardiologist.
Kevin SACK, Medtronic
Background: Optimizing patient pacing protocols is an ongoing endeavor aiming to provide more adaptive devices and ultimately improving patient quality of life. Atrial synchrony is not currently incorporated into pacing protocol considerations for AV delay. Some data exists showcasing that it should be, however, this is confounded by small sample size which may not overcome the biological variance that exists in the population. For patients with slower atrial activations, which can be recorded as longer p-wave duration, should AV delay be extended to provide hemodynamic benefit? We sought to answer this question using a computational electromechanical model of the heart, allowing us to vary P-wave duration in isolation of other factors.
Methods: An established 4-chamber beating human heart model was adapted to represent a typical HF patient with left bundle branch (LBB) pacing. Intrinsic activation of the atria was simulated with 4 different P-wave durations (i.e., due to different rates of atrial activation and depolarization) while keeping all other aspects of the model constant. Under this assumption, the impact of AV delay, i.e., the separation of atrial and LBB pacing activations by increments of 0ms, 40ms, 80ms, 120ms, 160ms, 200ms, 240ms, was assessed for hemodynamic and functional performance.
Results: Simulations compared stroke volume, maximum left ventricular pressure, maximum dPdt, and minimum left atrial mean pressure, which all revealed linear relationships (regression slopes of 0.88, 0.85, 1.09, and 0.88 respectively) between the P-wave duration and the optimal AV timing with LBB pacing.
Conclusion: Confirmation of a linear relationship between P-wave duration and optimal AV-delay in a simulated realistic four-chamber heart model strengthens the case to include P-wave duration in the design of adaptive pacing protocols.
Hao LIU, Abbott
Left bundle branch area pacing (LBBAP) is a novel and promising approach to improve cardiac synchronization and leads to better outcomes compared to conventional right ventricular pacing. However, long-term lead durability and safety remains an unknow area, which needs further investigation. In this study, an in-silico model was introduced to evaluate the fatigue risk of leads placed varying locations within the LBB. The bending stress and curvature of the lead conductors were extracted to evaluate the life-span based on goodman analysis. This technique also provides inputs for determining a clinically-relevant worst-case scenario fatigue test configuration. As a result, it is recommended to have “appropriate” amount of lead slack and lead depth in the septum for patients to create a balance between avoiding lead dislodgement and reducing lead mechanical failure. Moreover, implant locations and angles play an important role on fatigue risk of lead body, however their impacts also rely on patient-specific anatomy such as size and location of papillary muscles. Thus, with this approach, we are able to predict in-vivo condition of pacing lead behavior which provides insight into details of interactions between the lead and its surrounding tissue.
Sean FARRELL, Dassault Systèmes
Jillian FRIOT, Dassault Systèmes
Dr. Mohan THANIKACHALAM, Dynocardia
The 3DEXPERIENCE Lab is the global startup accelerator and innovation arm of Dassault Systèmes. Scouting the world for the most disruptive technology startups that align with at least one of the United Nations Sustainable Development Goals, our team provides access to the Dassault Systèmes suite of software solutions, technical and business mentorship, and marketing exposure.
In this session, we will discuss the 3DEXPERIENCE Lab value proposition and invite one of our star startups, Dynocardia, to the stage to discuss their cutting edge work in developing a continuous cuffless blood pressure monitoring device.
Jing BI, Dassault Systèmes
This presentation proposes a Neural Networks-based approach to quickly analyze 3D mitral valve deformation for patient prioritization. High-fidelity co-simulations are used as Design of Experiments to generate the comprehensive 3D data. The neural networks are trained to predict both transient responses and 3D deformation field and its history in seconds. This enables an information-rich semi-interactive software environment for making medical decisions. The methodology is further validated through 7 additional models covering diverse physics in structures and fluids domains.
Aneurysms in blood vessels, commonly seen in aorta and cerebrovascular network, can be severe health risk in patients as they alter hemodynamics, and are susceptible to rupture. One of the common treatment options is the insertion of flow-diverting braided stents used in intracranial aneurysms which aims to divert flow away from the aneurysm neck to reconstruct the parent artery and restore its natural course, reducing risk of rupture and risk to patient health.
Dassault Systèmes CAD & simulation brands have developed a modeling & simulation (MODSIM) approach for these flow-diverting stents applied to patient specific geometry. Dassault Systèmes CAD solution (CATIA) aims to provide streamlined process to create braided stent 3D geometry using common design parameters and then virtually morph the stent to patient-specific aneurysm geometry. The CAD solution provides seamless design iteration updates starting with a parametric value change through to the final shape. Dassault Systèmes Fluids (CFD) solution then aims to simulate stents in aneurysm with tight spacing under physiological conditions, applying robust meshing tools and advanced solver, to assess physiological parameters such as changes in flow rate into aneurysm chamber and wall shear stress before and after placement of the braided stent. In addition to providing detailed insights into local flow conditions, the flow parameters readily output from CFD model, can help to determine likelihood of rupture and thereby help in stent design improvements and potential treatment options.
Paul BRIANT, Exponent
Steve KREUZER, Exponent
Implanted medical devices experience a range of loading modes and magnitudes over their lifetime due to the many activities of daily living that humans perform. When combined with the variability across patients, the variations in load make identifying appropriate test parameters to assess implant complicated. Current practice in the field often simplifies the spectrum of loads to single loading mode and magnitude, which may involve either overestimating or underestimating the total fatigue load applied to the device. Recent computational approaches combining physics-based and statistical models under the umbrella of in silico trials can be harnessed to better predict the range of cyclic stresses and strains a device may undergo once implanted. However, to fully leverage this additional information, improved risk models are required for clinically relevant failure modes. To demonstrate this linkage, the use of in silico modeling to predict loading spectra for an implantable cardiac device is coupled with experimental results studying nitinol fatigue when subjected to a range of applied loads.
Dr. Johnathan WEISSMANN, Tel Aviv University
Heart failure with preserved ejection fraction (HFpEF), a prevalent global health condition, is characterized by normal ejection fraction but common diastolic impairment and elevated left ventricular filling pressures. Since pharmacological treatments have limited success, cardiac devices have been developed to
restore diastolic function. Among these devices is the transapical CORolla, a spring-like expander that transfers energy from systole to diastole, which has been
tested on animal models and a limited number of patients. In this study, we present finite element analyses of an HFpEF-induced swine to model the implantation of the CORolla in various configurations. We evaluate cardiac performance for each scenario and compare it to the preimplantation and healthy configurations to determine the device's effectiveness and potential use.
Dr. Ellen ROCHE, MIT
Cyclical dynamic expansion and contraction are essential to the life-sustaining function of organs, exemplified by the heart. These continuous movements coupled with complex tissue architecture and composite mechanical properties pose considerable challenges to augmenting impaired organ function. My research is providing paradigm-shifting approaches to overcome those challenges, by blending principles of pathophysiology, biomechanics and mechanical engineering with state-of-the-art materials and robotics. In this talk I will speak about developing physiologically realistic in vitro, in vivo, ex vivo and in silico approaches suitable for testing cardiac device technologies. I will review my group’s overarching approach to designing these technologies. I will discuss the potential impact of our work, and how co-designing multimodal simulation models with clinical and industrial partners can not only lead to enhanced implantable device design and testing, but also to further understanding of the fundamental mechanical influencers of pathophysiology and intervention strategies.
Ellen KUHL, Stanford University
Afrah SHAFQUAT, Dassault Systèmes
Medidata AI has access to over 30,000 clinical trials, 9 million patients. However, most clinical trial data remains siloed given concerns of patient privacy and clinical trial sponsor identity disclosure. Generative AI with applications like ChatGPT has broken barriers previously thought impossible. At Medidata, we are using AI to generate “synthetic clinical trial data” using our solution Simulants. This synthetic clinical trial data presents as a “Virtual Twin” for real clinical trial data, preserving the clinical insights, endpoints and outcomes of interest present in the real clinical trial data while most importantly, protecting patient privacy and trial sponsor anonymity. In this talk, we will discuss our AI solution Simulants, the power of enabling clinical trial data sharing across biotech and pharmaceutical companies and how synthetic and generative AI can accelerate the growth and establishment of a healthy and resilient Virtual Twin ecosystem.
Mathias PEIRLINCK, Delft University of Technology
We will highlight how machine learning approaches, coupled with multiscale modeling, hold important opportunities to improve our understanding of cardiac tissue behavior.
Melissa CERUOLO, Dassault Systèmes
With decades of expertise in physiological data analysis, Medidata Sensor Cloud can detect changes, map trends, and predict clinical outcomes. We are collaborating with our network partners to discover new digital biomarkers by transforming sensor data into objective endpoints to enrich the total view of the patient. Join this session to learn more about Sensor Cloud’s breadth of solutions, including quantifying quality of life, functional capacity, and movement – all of which can be fused with traditional clinical measures and ePRO. Walk away understanding how this rich data set of “virtual twins” from continuous physiological data enables innovative capabilities in digital phenotyping and personalized medicine.
Dr. Joe RIZZO, Harvard Medical School
Elahe JAVADI, Dassault Systèmes
Blindness is a relatively common disability that reduces the quality of life. Glaucoma and non-arteritic anterior ischemic optic neuropathy are the two most common forms of optic nerve blindness and both are due in part to disrupted blood flow in the optic nerve head (ONH). Remarkably, there is no 3D anatomical model of the human ONH and without this information, the impact of various parameters, including viscosity (related to serum protein levels), anemia (reduced red blood cells) or changes in blood pressure, intraocular pressure or cerebrospinal fluid pressure, each of which contributes to the perfusion pressure of the ONH, cannot be modeled. Our mission is to create the first 3D virtual twin of the Human Eye on the 3DEXPERIENCE Platform, Inc. to demonstrate the intricacies of the front (i.e. cornea, iris and lens) and back (i.e. retina and the optic nerve with its connection to the brain) of the eye. Application of the eye virtual twin model would be to explore the causes and to develop treatments for the two most common forms of optic nerve blindness.
Dr. Srinivasan JAYARAMAN, Tata Consultancy Services
Tata Consultancy Services Digital BioTwin – is a high-fidelity computational platform to constitute different organs of humans for investigating the medical device design and testing the safety and efficacy of drug products, including organ's function insight remotely in a non-invasively manner. TCS Digital Bio- twin (DBT) has systematically evolved over the years and exampled with digital skin, Heart (CardioByte), Colon, and Nasal system that captures the details of the organs from radiographic image and offers signiﬁcant mechanistic insight that compliments experimental observations with direct 3D interaction using advanced visualization (AR). It has the same degree of rigor and evidence, reducing human enrollment by a sizable percentage. DBT resulted in the first-time right, minimized cost and time, minimized OT time, and accelerated surgical efficiency, improving the customer's experience (surgeon and patient).
For this Symposium, our focus will be the Digital twin of the Nasal Cavity. It has been reported that approximately 13% of adults (29.3 million people) in the US and 11% of the European population suffer from obstruction of nasal breathing, swelling, or inflammation of the nasal sinuses; it is estimated that approximately $5.8 billion is spent annually on surgery to relieve nasal obstruction.
On the other hand, Nasal drug delivery has gained increased attention in recent years due to its potential advantages over traditional oral or parenteral routes. The nasal epithelium, a highly permeable monolayer, efficiently absorbs a wide range of chemically diverse drugs. In addition, the non-invasive nature of nasal drug delivery makes it a promising option for long-term systemic administration, as it affords patient comfort and compliance often hindered by other types of drug therapy. For an instant, the nasal route provides a direct and rapid pathway to systemic circulation by avoiding first-pass metabolism in the liver. Further, the nasal route has proven to be a viable means of administration for drugs otherwise degraded in the GI tract, such as peptides and proteins.
Therefore, understanding three-dimensional nasal airway anatomy is crucial for both the pharma and health care sector.
TCS's Digital Twin of the Nasal platform enables the investigation of the mechanistic insight of the nasal airway, reconstructed from a tomographic image and morphometric analysis of the nasal with direct 3D interaction using advanced visualization (AR). This holistic cloud-enabled Nasal platform serves as in silico model for pharma, clinical diagnosis and therapy, and regulatory bodies. The nasal twin platform is complemented with the ML model to accelerate targeted nasal drug discovery, in addition to testing the drug efficiency and digital evidence, aiming to reduce animal testing or human in the trial.
The extension of the twin platform allows morphological modification, a virtual surgical platform on the 3D nasal model, mimicking clinical surgery and its impact. The resulting system function as an armamentarium for the Rhinologists, which will help optimize technique and time and enhance the postoperative surgical efficiency and the customer's experience (surgeon and patient) with the same degree of rigor and evidence. Thus, the DBT resulted in the first-time right, minimized cost and time, minimized OT time, and accelerated surgical efficiency.
Alireza HEIDARI, McGill University
Heart failure (HF) and acute myocardial infarction are among the main causes of disability and death worldwide. It has been shown that mitochondrial dysfunction has a key role in the pathogenesis of heart ischemia, cardiomyopathy, and reperfusion injury. Most CVDs and HFs are associated with impaired mitochondrial function. As such, new treatments and biological drugs are needed, and mitochondria are important target for CVD and HF treatments. In this study the computational modeling of heart failure for three patients before and after the co-transplantation intracoronary and intra-myocardial injection of exosomes and mitochondria are presented.
Owen RICHFIELD, Yale University
Kidney disease is a silent epidemic affecting 15% of the US population, and kidney dysfunction is intimately associated with cardiovascular diseases through the kidney's vital role in blood pressure regulation. Despite the importance of the kidney in myriad vital physiological processes (fluid volume homeostasis, acid-base balance, electrolyte homeostasis, erythropoietin production) there are few therapies targeted to kidney diseases. This lack of innovation can be attributed to numerous factors, including a fundamental lack of understanding of how the kidney functions in three dimensions; the functional unit of the kidney, the nephron, is a complex and specialized anatomical/physiological structure that performs the vital roles of filtration of the blood to produce a concentrated urine. The nephron's function has been studied in detail for decades, however the kidney is composed of ~1 million nephrons functioning in close proximity, which introduces less well understood physiological and physical interactions between nephrons. By modeling an entire kidney in 3D, a virtual twin of the kidney has the potential to not only identify therapies to reduce kidney disease, but also improve our understanding of how the kidney works. The development of the virtual twin of the kidney faces numerous substantial technical challenges that will require an interdisciplinary engineering approach to solve. These challenges include the implementation of a mathematical formalism to describe the kidney's function and physiological relationship with the rest of the body, as well as technologies required to obtain patient data to accurately model their kidney (anatomy, function and pathology) in real time.