Patient Specific Computer Modelling for Automated Sizing of Fenestrated Stent Grafts

Digital twin and artificial intelligence technologies for predictive planning of endovascular procedures

Current planning of aortic and peripheral endovascular procedures is based largely on manual measurements performed from the 3-dimensional reconstruction of preoperative computed tomography scans. Assessment of device behavior inside patient anatomy is often difficult, and available tools, such as 3-dimensional-printed models, have several limitations. Digital twin (DT) technology has been used successfully in automotive and aerospace industries and applied recently to endovascular aortic aneurysm repair. Artificial intelligence allows the treatment of large amounts of data, and its use in medicine is increasing rapidly. The aim of this review was to present the current status of DTs combined with artificial intelligence for planning endovascular procedures. Patient-specific DTs of the aorta are generated from preoperative computed tomography and integrate aorta mechanical properties using finite element analysis. The same methodology is used to generate 3-dimensional models of aortic stent-grafts and simulate their deployment. Post processing of DT models is then performed to generate multiple parameters related to stent-graft oversizing and apposition. Machine learning algorithms allow parameters to be computed into a synthetic index to predict Type 1A endoleak risk. Other planning and sizing applications include custom-made fenestrated and branched stent-grafts for complex aneurysms. DT technology is also being investigated for planning peripheral endovascular procedures, such as carotid artery stenting. DT provides detailed information on endovascular device behavior. Analysis of DT-derived parameters with machine learning algorithms may improve accuracy in predicting complications, such as Type 1A endoleaks.

Computational surgery in the management of patients with abdominal aortic aneurysms: Opportunities, challenges, and future directions

Computational surgery (CS) is an interdisciplinary field that uses mathematical models and algorithms to focus specifically on operative planning, simulation, and outcomes analysis to improve surgical care provision. As the digital revolution transforms the surgical work environment through broader adoption of artificial intelligence and machine learning, close collaboration between surgeons and computational scientists is not only unavoidable, but will become essential. In this review, the authors summarize the main advances, as well as ongoing challenges and prospects, that surround the implementation of CS techniques in vascular surgery, with a particular focus on the care of patients affected by abdominal aortic aneurysms (AAAs). Several key areas of AAA care delivery, including patient-specific modelling, virtual surgery simulation, intraoperative imaging-guided surgery, and predictive analytics, as well as biomechanical analysis and machine learning, will be discussed. The overarching goals of these CS applications is to improve the precision and accuracy of AAA repair procedures, while enhancing safety and long-term outcomes. Accordingly, CS has the potential to significantly enhance patient care across the entire surgical journey, from preoperative planning and intraoperative decision making to postoperative surveillance. Moreover, CS-based approaches offer promising opportunities to augment AAA repair quality by enabling precise preoperative simulations, real-time intraoperative navigation, and robust postoperative monitoring. However, integrating these advanced computer-based technologies into medical research and clinical practice presents new challenges. These include addressing technical limitations, ensuring accuracy and reliability, and managing unique ethical considerations associated with their use. Thorough evaluation of these aspects of advanced computation techniques in AAA management is crucial before widespread integration into health care systems can be achieved.

Evolutionary trends and innovations in cardiovascular intervention

Cardiovascular diseases remain a global health challenge, prompting continuous innovation in medical technology, particularly in Cardiovascular MedTech. This article provides a comprehensive exploration of the transformative landscape of Cardiovascular MedTech in the 21st century, focusing on interventions. The escalating prevalence of cardiovascular diseases and the demand for personalized care drive the evolving landscape, with technologies like wearables and AI reshaping patient-centric healthcare. Wearable devices offer real-time monitoring, enhancing procedural precision and patient outcomes. AI facilitates risk assessment and personalized treatment strategies, revolutionizing intervention precision. Minimally invasive procedures, aided by robotics and novel materials, minimize patient impact and improve outcomes. 3D printing enables patient-specific implants, while regenerative medicine promises cardiac regeneration. Augmented reality headsets empower surgeons during procedures, enhancing precision and awareness. Novel materials and radiation reduction techniques further optimize interventions, prioritizing patient safety. Data security measures ensure patient privacy in the era of connected healthcare. Modern technologies enhance traditional surgeries, refining outcomes. The integration of these innovations promises to shape a healthier future for cardiovascular procedures, emphasizing collaboration and research to maximize their transformative potential.

Potential of computational models in personalized treatment of obstructive sleep apnea: a patient-specific partial 3D finite element study

The upper airway experiences mechanical loads during breathing. Obstructive sleep apnea is a very common sleep disorder, in which the normal function of the airway is compromised, enabling its collapse. Its treatment remains unsatisfactory with variable efficacy in the case of many surgeries. Finite element models of the upper airway to simulate the effects of various anatomic and physiologic manipulations on its mechanics could be helpful in predicting surgical success. Partial 3D finite element models based on patient-specific CT-scans were undertaken in a pilot study of 5 OSA patients. Upper airway soft tissues including the soft palate, hard palate, tongue, and pharyngeal wall were segmented around the midsagittal plane up to a width of 2.5 cm in the lateral direction. Simulations of surgical interventions such as Uvulopalatopharyngoplasty (UPPP), maxillo-mandibular advancement (MMA), palatal implants, and tongue implants have been performed. Our results showed that maxillo-mandibular advancement (MMA) surgery of 1 cm improved the critical closing pressure by at least 212.2%. Following MMA, the best improvement was seen via uvulopalatopharyngoplasty (UPPP), with an improvement of at least 19.12%. Palatal and tongue implants also offered a certain degree of improvement. Further, we observed possible interacting mechanisms that suggested simultaneous implementation of UPPP and tongue stiffening; and palatal and tongue stiffening could be beneficial. Our results suggest that computational modeling is a useful tool for analyzing the influence of anatomic and physiological manipulations on upper airway mechanics. The goal of personalized treatment in the case of OSA could be achieved with the use of computational modeling.

Computational prediction of proximal sealing in endovascular abdominal aortic aneurysm repair with unfavorable necks

BACKGROUND AND OBJECTIVE

Endovascular aortic aneurysm repair (EVAR) has become the standard treatment for abdominal aortic aneurysms in most centers. However, proximal sealing complications leading to endoleaks and migrations sometimes occur, particularly in unfavorable aortic anatomies and are strongly dependent on biomechanical interactions between the aortic wall and the endograft. The objective of the present work is to develop and validate a computational patient-specific model that can accurately predict these complications.

METHODS

Based on pre-operative CT-scans, we developed finite element models of the aorta of 10 patients who underwent endovascular aortic aneurysm repair, 7 with standard morphologies and 3 with unfavorable anatomies. We simulated the deployment of stent grafts in each aorta by solving mechanical equilibrium with a virtual shell method. Eventually we compared the actual stent ring positions from post-operative computed-tomography-scans with the predicted simulated positions.

RESULTS

A successful deployment simulation could be performed for each patient. Relative radial, transverse and longitudinal deviations were 6.3 ± 4.4%, 2.5 ± 0.9 mm and 1.4 ± 1.1 mm, respectively.

CONCLUSIONS

The numerical model predicted accurately stent-graft positions in the aortic neck of 10 patients, even in complex anatomies. This shows the potential of computer simulation to anticipate possible proximal endoleak complications before EVAR interventions.

Towards biomechanics-based pre-procedural planning for thoracic endovascular aortic repair of aortic dissection

BACKGROUND AND OBJECTIVE

Although thoracic aortic endovascular repair (TEVAR) has shown promising outcomes in the treatment of patients with complicated type B aortic dissection, complications still occur after TEVAR that can lead to catastrophic events. Biomechanical interactions between the stent-graft (SG) and the local aortic tissue play a critical role in determining the outcome of TEVAR. Different SG design may cause different biomechanical responses in the treated aorta, but such information is not known at the time of pre-procedural planning. By developing patient-specific virtual stent-graft deployment tools, it is possible to analyse and compare the biomechanical impact of different SGs on the local aorta for individual patients.

METHODS

A finite element based virtual SG deployment model was employed in this study. Computational simulations were performed on a patient-specific model of type B aortic dissection, accounting for details of the SG design and the hyperelastic behaviour of the aortic wall. Based on the geometry reconstructed from the pre-TEVAR CTA scan, the patient-specific aortic dissection model was created and pre-stressed. Parametric models of three different SG products (SG1, SG2 and SG3) were built with two different lengths for each design. The SG models incorporated different stent and graft materials, stent strut patterns, and assembly approaches. Using our validated SG deployment simulation framework, virtual trials were performed on the patient-specific aortic dissection model using different SG products and varying SG lengths.

CONCLUSION

Simulation results for different SG products suggest that SG3 with a longer length (SG3-long) would be the most appropriate device for the individual patient. Compared to SG1-short (the SG deployed in the patient), SG3-long followed the true lumen tortuosity closely, resulted in a more uniform true lumen expansion and a significant reduction in peak stress in the distal landing zone. These simulation results are promising and demonstrate the feasibility of using the virtual SG deployment model to assist clinicians in pre-procedural planning.

Artificial intelligence in vascular surgical decision making

Despite advances in prevention, detection, and treatment, cardiovascular disease is a leading cause of mortality and represents a major health problem worldwide. Artificial intelligence and machine learning have brought new insights to the management of vascular diseases by allowing analysis of huge and complex datasets and by offering new techniques to develop advanced imaging analysis. Artificial intelligence-based applications have the potential to improve prognostic evaluation and evidence-based decision making and contribute to vascular therapeutic decision making. In this scoping review, we provide an overview on how artificial intelligence could help in vascular surgical clinical decision making, highlighting potential benefits, current limitations, and future challenges.

Impact of thoracic endovascular aortic repair on aortic biomechanics: Integration of in silico and ex vivo analysis using porcine model

Thoracic endovascular aortic repair (TEVAR) is widespread in clinical practice for treating aortic diseases but it has relevant systemic complications, such as increase of the cardiac workload due to post-TEVAR aortic stiffening, and local issues such as re-entry tears due to the tissue damage caused by endograft interaction. The present study aims to elucidate these aortic biomechanical mechanisms by coupling ex vivo and in silico analysis. By ex vivo tests, the pulse wave velocity before and after TEVAR is measured. Uni-axial tensile tests are performed to measure regional mechanical response of tissue samples, supplied as input data for the in silico analysis. Numerical analysis is finally performed to compute the wall stress induced by the stent-graft deployment and the arterial pressurization. The ex vivo results highlight an increase of baseline PWV by a mean .78 m/s or 12% after TEVAR with a 100 mm stent-graft (p <.013). In the in silico analysis, the average von Mises stress in the landing zone increases of about 15% and 20% using, respectively stent-graft with radial oversizing of 10% and 20%. This work shows the effectiveness of integrated framework to analyze the biomechanical post TEVAR mechanisms. Moreover, the obtained results quantify the effect of prosthesis selection on the stiffening of the aorta after TEVAR and on the local increase of the aortic wall stress that is proportional to the stent-graft oversizing.

Patient-Specific Numerical Simulations of Endovascular Procedures in Complex Aortic Pathologies: Review and Clinical Perspectives

The endovascular technique is used in the first line treatment in many complex aortic pathologies. Its clinical outcome is mostly determined by the appropriate selection of a stent-graft for a specific patient and the operator's experience. New tools are still needed to assist practitioners with decision making before and during procedures. For this purpose, numerical simulation enables the digital reproduction of an endovascular intervention with various degrees of accuracy. In this review, we introduce the basic principles and discuss the current literature regarding the use of numerical simulation for endovascular management of complex aortic diseases. Further, we give the future direction of everyday clinical applications, showing that numerical simulation is about to revolutionize how we plan and carry out endovascular interventions.

Precision medicine using patient-specific modelling: state of the art and perspectives in dental practice

The dental practice has largely evolved in the last 50 years following a better understanding of the biomechanical behaviour of teeth and its supporting structures, as well as developments in the fields of imaging and biomaterials. However, many patients still encounter treatment failures; this is related to the complex nature of evaluating the biomechanical aspects of each clinical situation due to the numerous patient-specific parameters, such as occlusion and root anatomy. In parallel, the advent of cone beam computed tomography enabled researchers in the field of odontology as well as clinicians to gather and model patient data with sufficient accuracy using image processing and finite element technologies. These developments gave rise to a new precision medicine concept that proposes to individually assess anatomical and biomechanical characteristics and adapt treatment options accordingly. While this approach is already applied in maxillofacial surgery, its implementation in dentistry is still restricted. However, recent advancements in artificial intelligence make it possible to automate several parts of the laborious modelling task, bringing such user-assisted decision-support tools closer to both clinicians and researchers. Therefore, the present narrative review aimed to present and discuss the current literature investigating patient-specific modelling in dentistry, its state-of-the-art applications, and research perspectives.

A Statistical Shape Model of the Morphological Variation of the Infrarenal Abdominal Aortic Aneurysm Neck

Hostile aortic neck characteristics, such as short length and large diameter, have been associated with type Ia endoleaks and reintervention after endovascular aneurysm repair (EVAR). However, such characteristics partially describe the complex aortic neck morphology. A more comprehensive quantitative description of 3D neck shape might lead to new insights into the relationship between aortic neck morphology and EVAR outcomes in individual patients. This study identifies the 3D morphological shape components that describe the infrarenal aortic neck through a statistical shape model (SSM). Pre-EVAR CT scans of 97 patients were used to develop the SSM. Parameterization of the morphology was based on the center lumen line reconstruction, a triangular surface mesh of the aortic lumen, 3D coordinates of the renal arteries, and the distal end of the aortic neck. A principal component analysis of the parametrization of the aortic neck coordinates was used as input for the SSM. The SSM consisted of 96 principal components (PCs) that each described a unique shape feature. The first five PCs represented 95% of the total morphological variation in the dataset. The SSM is an objective model that provides a quantitative description of the neck morphology of an individual patient.

Patient-specific computational modeling of endovascular aneurysm repair: State of the art and future directions

Endovascular aortic repair (EVAR) has become the preferred intervention option for aortic aneurysms and dissections. This is because EVAR is much less invasive than the alternative open surgery repair. While in-hospital mortality rates are smaller for EVAR than open repair (1%-2% vs. 3%-5%), the early benefits of EVAR are lost after 3 years due to larger rates of complications in the EVAR group. Clinicians follow instructions for use (IFU) when possible, but are left with personal experience on how to best proceed and what choices to make with respect to stent-graft (SG) model choice, sizing, procedural options, and their implications on long-term outcomes. Computational modeling of SG deployment in EVAR and tissue remodeling after intervention offers an alternative way of testing SG designs in silico, in a personalized way before intervention, to ultimately select the strategies leading to better outcomes. Further, computational modeling can be used in the optimal design of SGs in cases of complex geometries. In this review, we address some of the difficulties and successes associated with computational modeling of EVAR procedures. There is still work to be done in all areas of EVAR in silico modeling, including model validation, before models can be applied in the clinic, but much progress has already been made. Critical to clinical implementation are current efforts focusing on developing fast algorithms that can achieve (near) real-time solutions, as well as ways of dealing with inherent uncertainties related to patient aortic wall degradation on an individualized basis. We are optimistic that EVAR modeling in the clinic will soon become a reality to help clinicians optimize EVAR interventions and ultimately reduce EVAR-associated complications.

Assessment of fenestrated Anaconda stent graft design by numerical simulation: Results of a European prospective multicenter study

OBJECTIVE

A crucial step in designing fenestrated stent grafts for treatment of complex aortic abdominal aneurysms is the accurate positioning of the fenestrations. The deployment of a fenestrated stent graft prototype in a patient-specific rigid aortic model can be used for design verification in vitro, but is time and human resources consuming. Numerical simulation (NS) of fenestrated stent graft deployment using the finite element analysis has recently been developed; the aim of this study was to compare the accuracy of fenestration positioning by NS and in vitro.

METHODS

All consecutive cases of complex aortic abdominal aneurysm treated with the Fenestrated Anaconda (Terumo Aortic) in six European centers were included in a prospective, observational study. To compare fenestration positioning, the distance from the center of the fenestration to the proximal end of the stent graft (L) and the angular distance from the 0° position (C) were measured and compared between in vitro testing (L1, C1) and NS (L2, C2). The primary hypothesis was that ΔL (|L2 - L1|) and ΔC (|C2 - C1|) would be 2.5 or less mm in more than 80% of the cases. The duration of both processes was also compared.

RESULTS

Between May 2018 and January 2019, 50 patients with complex aortic abdominal aneurysms received a fenestrated stent graft with a total of 176 fenestrations. The ΔL and ΔC was 2.5 mm or less for 173 (98%) and 174 (99%) fenestrations, respectively. The NS process duration was significantly shorter than the in vitro (2.1 days [range, 1.0-5.2 days] vs 20.6 days [range, 9-82 days]; P < .001).

CONCLUSIONS

Positioning of fenestrations using NS is as accurate as in vitro and could significantly decrease delivery time of fenestrated stent grafts.

Patient-Specific Virtual Stent-Graft Deployment for Type B Aortic Dissection: A Pilot Study of the Impact of Stent-Graft Length

Thoracic endovascular aortic repair (TEVAR) has been accepted as a standard treatment option for complicated type B aortic dissection. Distal stent-graft-induced new entry (SINE) is recognised as one of the main post-TEVAR complications, which can lead to fatal prognosis. Previous retrospective cohort studies suggested that short stent-graft (SG) length (<165 mm) might correlate with increased risk of distal SINE. However, the influence of SG length on changes in local biomechanical conditions before and after TEVAR is unknown. In this paper, we aim to address this issue using a virtual SG deployment simulation model developed for application in type B aortic dissection. Our model incorporates detailed SG design and hyperelastic behaviour of the aortic wall. By making use of patient-specific geometry reconstructed from pre-TEVAR computed tomography angiography (CTA) scan, our model can predict post-TEVAR SG configuration and wall stress. Virtual SG deployment simulations were performed on a patient who underwent TEVAR with a short SG (158 mm in length), mimicking the actual clinical procedure. Further simulations were carried out on the same patient geometry but with different SG lengths (183 mm and 208 mm) in order to evaluate the effect of SG length on changes in local stress in the treated aorta. Comparisons of simulation results for different SG lengths showed the location of maximum stress varied with the SG length. With the short SG (deployed in the patient), the maximum von Mises stress of 238.9 kPa was found on the intimal flap at the distal landing zone where SINE was identified at 3-month follow-up. Increasing the SG length caused the maximum von Mises stress to move away from the distal landing zone where stress values were reduced by approximately 17% with the medium-length SG and by 60% with the long SG. This pilot study demonstrates the potential of using the virtual SG deployment model as a pre-surgical planning tool to help select the most appropriate SG length for individual patients.

Finite element modeling to predict procedural success of thoracic endovascular aortic repair in type A aortic dissection

Objective

Thoracic endovascular aortic repair (TEVAR) is recommended for type B aortic dissection and recently has even been used in selected cases of proximal (Stanford type A) aortic dissections in scenarios of prohibitive surgical risk. However, mechanical interactions between the native aorta and stent-graft are poorly understood, as some cases ended in failure. The aim of this study is to explore and better understand biomechanical changes after TEVAR and predict the result via virtual stenting.

Methods

A case of type A aortic dissection was considered inoperable and selected for TEVAR. The procedure failed due to stent-graft migration even with precise deployment. A novel patient-specific virtual stent-graft deployment model based on finite element method was employed to analyze TEVAR-induced changes under such conditions. Two landing positions were simulated to investigate the reason for stent-graft migration immediately after TEVAR and explore options for optimization.

Results

Simulation of the actual procedure revealed that the proximal bare metal stent pushed the lamella into the false lumen and led to further stent-graft migration during the launch phase. An alternative landing position has reduced the local deformation of the dissection lamella and avoided stent-graft migration. Higher maximum principal stress (>20 KPa) was found on the lamella with deployment at the actual position, while the alternative strategy would have reduced the stress to <5 KPa.

Conclusions

Virtual stent-graft deployment simulation based on finite element model could be helpful to both predict outcomes of TEVAR and better plan future endovascular procedures.

Aneurysms and dissections - What is new in the literature of 2019/2020 - a European Society of Vascular Medicine annual review

More than 6,000 publications were found in PubMed concerning aneurysms and dissections, including those Epub ahead of print in 2019, printed in 2020. Among those publications 327 were selected and considered of particular interest.