Application of Digital Tools and Artificial Intelligence to the Myasthenia Gravis Core Examination.

Background: Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation through objective digital evaluation. Objectives: We assessed ability to quantitate Zoom video recordings of a standardized neurological examination the myasthenia gravis core examination (MG-CE), which had been designed for telemedicine evaluations. Methods: We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. Computer vision in combination with artificial intelligence methods were used to build algorithms to analyze videos with a focus on eye or body motions. For the assessment of examinations involving vocalization, signal processing methods were developed, including natural language processing. A series of algorithms were built that could automatically compute the metrics of the MG-CE. Results: Fifty-one patients with MG with videos recorded twice on separate days and 15 control subjects were assessed once. We were successful in quantitating lid, eye, and arm positions and as well as well as develop respiratory metrics using breath counts. Cheek puff exercise was found to be of limited value for quantitation. Technical limitations included variations in illumination, bandwidth, and recording being done on the examiner side, not the patient. Conclusions: Several aspects of the MG-CE can be quantitated to produce continuous measures via standard Zoom video recordings. Further development of the technology offer the ability for trained, non-physician, health care providers to perform precise examination of patients with MG outside the clinic, including for clinical trials. Plain Language Summary: Advances in video image analysis and artificial intelligence provide the opportunity to transform the approach to patient evaluation. Here, we asked whether video recordings of the typical telemedicine examination for the patient with myasthenia gravis be used to quantitate examination findings? Despite recordings not made for purpose, we were able to develop and apply computer vision and artificial intelligence to Zoom recorded videos to successfully quantitate eye muscle, facial muscle, and limb fatigue. The analysis also pointed out limitations of human assessments of bulbar and respiratory assessments. The neuromuscular examination can be enhanced by advance technologies, which have the promise to improve clinical trial outcome measures as well as standard care.

Eye Segmentation Method for Telehealth: Application to the Myasthenia Gravis Physical Examination.

Due to the precautions put in place during the COVID-19 pandemic, utilization of telemedicine has increased quickly for patient care and clinical trials. Unfortunately, teleconsultation is closer to a video conference than a medical consultation, with the current solutions setting the patient and doctor into an evaluation that relies entirely on a two-dimensional view of each other. We are developing a patented telehealth platform that assists with diagnostic testing of ocular manifestations of myasthenia gravis. We present a hybrid algorithm combining deep learning with computer vision to give quantitative metrics of ptosis and ocular muscle fatigue leading to eyelid droop and diplopia. The method works both on a fixed image and frame by frame of the video in real-time, allowing capture of dynamic muscular weakness during the examination. We then use signal processing and filtering to derive robust metrics of ptosis and l ocular misalignment. In our construction, we have prioritized the robustness of the method versus accuracy obtained in controlled conditions in order to provide a method that can operate in standard telehealth conditions. The approach is general and can be applied to many disorders of ocular motility and ptosis.

A Digital Telehealth System to Compute the Myasthenia Gravis Core Examination Metrics.

Background: Telemedicine practice for neurological diseases has grown significantly during the COVID-19 pandemic.Telemedicine offers an opportunity to assess digitalization of examinations and enhances access to modern computer vision and artificial intelligence processing to annotate and quantify examinations in a consistent and reproducible manner. The Myasthenia Gravis Core Examination (MG-CE) has been recommended for the telemedicine evaluation of patients with myasthenia gravis. Objective: We aimed to assess the ability to take accurate and robust measurements during the examination, which would allow improvement in workflow efficiency by making the data acquisition and analytics fully automatic and thereby limit the potential for observation bias. Methods: We used Zoom (Zoom Video Communications) videos of patients with myasthenia gravis undergoing the MG-CE. The core examination tests required 2 broad categories of processing. First, computer vision algorithms were used to analyze videos with a focus on eye or body motions. Second, for the assessment of examinations involving vocalization, a different category of signal processing methods was required. In this way, we provide an algorithm toolbox to assist clinicians with the MG-CE. We used a data set of 6 patients recorded during 2 sessions. Results: Digitalization and control of quality of the core examination are advantageous and let the medical examiner concentrate on the patient instead of managing the logistics of the test. This approach showed the possibility of standardized data acquisition during telehealth sessions and provided real-time feedback on the quality of the metrics the medical doctor is assessing. Overall, our new telehealth platform showed submillimeter accuracy for ptosis and eye motion. In addition, the method showed good results in monitoring muscle weakness, demonstrating that continuous analysis is likely superior to pre-exercise and post-exercise subjective assessment. Conclusions: We demonstrated the ability to objectively quantitate the MG-CE. Our results indicate that the MG-CE should be revisited to consider some of the new metrics that our algorithm identified. We provide a proof of concept involving the MG-CE, but the method and tools developed can be applied to many neurological disorders and have great potential to improve clinical care.

Gastroenterology Procedures Generate Aerosols: An Air Quality Turnover Solution to Mitigate COVID-19's Propagation Risk.

The growing fear of virus transmission during the 2019 coronavirus disease (COVID-19) pandemic has called for many scientists to look into the various vehicles of infection, including the potential to travel through aerosols. Few have looked into the issue that gastrointestinal (GI) procedures may produce an abundance of aerosols. The current process of risk management for clinics is to follow a clinic-specific HVAC formula, which is typically calculated once a year and assumes perfect mixing of the air within the space, to determine how many minutes each procedural room refreshes 99% of its air between procedures when doors are closed. This formula is not designed to fit the complex dynamic of small airborne particle transport and deposition that can potentially carry the virus in clinical conditions. It results in reduced procedure throughput as well as an excess of idle time in clinics that process a large number of short procedures such as outpatient GI centers. We present and tested a new cyber-physical system that continuously monitors airborne particle counts in procedural rooms and also at the same time automatically monitors the procedural rooms' state and flexible endoscope status without interfering with the clinic's workflow. We use our data gathered from over 1500 GI cases in one clinical suite to understand the correlation between air quality and standard procedure types as well as identify the risks involved with any HVAC system in a clinical suite environment. Thanks to this system, we demonstrate that standard GI procedures generate large quantities of aerosols, which can potentially promote viral airborne transmission among patients and healthcare staff. We provide a solution for the clinic to improve procedure turnover times and throughput, as well as to mitigate the risk of airborne transmission of the virus.

A model of workflow in the hospital during a pandemic to assist management.

We present a computational model of workflow in the hospital during a pandemic. The objective is to assist management in anticipating the load of each care unit, such as the ICU, or ordering supplies, such as personal protective equipment, but also to retrieve key parameters that measure the performance of the health system facing a new crisis. The model was fitted with good accuracy to France's data set that gives information on hospitalized patients and is provided online by the French government. The goal of this work is both practical in offering hospital management a tool to deal with the present crisis of COVID-19 and offering a conceptual illustration of the benefit of computational science during a pandemic.

A Systems Approach to Assess Transport and Diffusion of Hazardous Airborne Particles in a Large Surgical Suite: Potential Impacts on Viral Airborne Transmission.

Airborne transmission of viruses, such as the coronavirus 2 (SARS-CoV-2), in hospital systems are under debate: it has been shown that transmission of SARS-CoV-2 virus goes beyond droplet dynamics that is limited to 1 to 2 m, but it is unclear if the airborne viral load is significant enough to ensure transmission of the disease. Surgical smoke can act as a carrier for tissue particles, viruses, and bacteria. To quantify airborne transmission from a physical point of view, we consider surgical smoke produced by thermal destruction of tissue during the use of electrosurgical instruments as a marker of airborne particle diffusion-transportation. Surgical smoke plumes are also known to be dangerous for human health, especially to surgical staff who receive long-term exposure over the years. There are limited quantified metrics reported on long-term effects of surgical smoke on staff's health. The purpose of this paper is to provide a mathematical framework and experimental protocol to assess the transport and diffusion of hazardous airborne particles in every large operating room suite. Measurements from a network of air quality sensors gathered during a clinical study provide validation for the main part of the model. Overall, the model estimates staff exposure to airborne contamination from surgical smoke and biological material. To address the clinical implication over a long period of time, the systems approach is built upon previous work on multi-scale modeling of surgical flow in a large operating room suite and takes into account human behavior factors.

A fully coupled computational fluid dynamics - agent-based model of atherosclerotic plaque development: Multiscale modeling framework and parameter sensitivity analysis.

BACKGROUND: Peripheral Artery Disease (PAD) is an atherosclerotic disorder that leads to impaired lumen patency through intimal hyperplasia and the build-up of plaques, mainly localized in areas of disturbed flow. Computational models can provide valuable insights in the pathogenesis of atherosclerosis and act as a predictive tool to optimize current interventional techniques. Our hypothesis is that a reliable predictive model must include the atherosclerosis development history. Accordingly, we developed a multiscale modeling framework of atherosclerosis that replicates the hemodynamic-driven arterial wall remodeling and plaque formation. METHODS: The framework was based on the coupling of Computational Fluid Dynamics (CFD) simulations with an Agent-Based Model (ABM). The CFD simulation computed the hemodynamics in a 3D artery model, while 2D ABMs simulated cell, Extracellular Matrix (ECM) and lipid dynamics in multiple vessel cross-sections. A sensitivity analysis was also performed to evaluate the oscillation of the ABM output to variations in the inputs and to identify the most influencing ABM parameters. RESULTS: Our multiscale model qualitatively replicated both the physiologic and pathologic arterial configuration, capturing histological-like features. The ABM outputs were mostly driven by cell and ECM dynamics, largely affecting the lumen area. A subset of parameters was found to affect the final lipid core size, without influencing cell/ECM or lumen area trends. CONCLUSION: The fully coupled CFD-ABM framework described atherosclerotic morphological and compositional changes triggered by a disturbed hemodynamics.

Computing patient-specific hemodynamics in stented femoral artery models obtained from computed tomography using a validated 3D reconstruction method.

Patients with peripheral artery disease who undergo endovascular treatment are often inflicted by in-stent restenosis. The relation between restenosis and abnormal hemodynamics may be analyzed using patient-specific computational fluid dynamics (CFD) simulations. In this work, first a three-dimensional (3D) reconstruction method, based on an in-house semi-automatic segmentation algorithm of a patient's computed tomography (CT) images with calcification and metallic artifacts, and thrombus removal is described. The reconstruction method was validated using 3D printed rigid phantoms of stented femoral arteries by comparing the reconstructed geometries with the reference computer-aided design (CAD) geometries employed for 3D printing. The mean reconstruction error resulting from the validation of the reconstruction method was ~6% in both stented and non-stented regions. Secondly, a patient-specific model of the stented femoral artery was created and CFD analyses were performed with emphasis on the selection of the boundary conditions. CFD results were compared in scenarios with and without common femoral artery bifurcation, employing flat or parabolic inlet velocity profiles. Similar helical flow structures were visible in all scenarios. Negligible differences in wall shear stress (<0.5%) were found in the stented region. In conclusion, a robust method, applicable to patient-specific cases of stented diseased femoral arteries, was developed and validated.

A Continuum of Interfaces to Engage Surgical Staff in Efficient Collaboration.

Large hospital surgical suites must combine high quality of care with an efficient management of operations. However, the diversity of procedures, staff, and patients present important challenges for staff collaboration. The complexity of flows between tasks and places, such as interconnections between pre-operation, post-operation and intensive care units, led previous research to address these issues separately using checklists, scheduling, or specialized human-computer interfaces. Approaches to treat the surgical suite as a whole entity have not been explored yet. Here, we build upon a cyber-physical system comprising an electronic whiteboard and different sensors tracking the status of operating rooms to design a continuum of mobile and fixed, shared computer interfaces. The interfaces disseminate the information through different locations and devices and allow for its manipulation in order to foster appropriate collaboration on unforeseen events and decisions. We present our design rationale process, involving the different surgical suite users and stakeholders and report on the architecture of the system.

A versatile hybrid agent-based, particle and partial differential equations method to analyze vascular adaptation.

Peripheral arterial occlusive disease is a chronic pathology affecting at least 8-12 million people in the USA, typically treated with a vein graft bypass or through the deployment of a stent in order to restore the physiological circulation. Failure of peripheral endovascular interventions occurs at the intersection of vascular biology, biomechanics, and clinical decision making. It is our hypothesis that the majority of endovascular treatment approaches share the same driving mechanisms and that a deep understanding of the adaptation process is pivotal in order to improve the current outcome of the procedure. The postsurgical adaptation of vein graft bypasses offers the perfect example of how the balance between intimal hyperplasia and wall remodeling determines the failure or the success of the intervention. Accordingly, this work presents a versatile computational model able to capture the feedback loop that describes the interaction between events at cellular/tissue level and mechano-environmental conditions. The work here presented is a generalization and an improvement of a previous work by our group of investigators, where an agent-based model uses a cellular automata principle on a fixed hexagonal grid to reproduce the leading events of the graft's restenosis. The new hybrid model here presented allows a more realistic simulation both of the biological laws that drive the cellular behavior and of the active role of the membranes that separate the various layers of the vein. The novel feature is to use an immersed boundary implementation of a highly viscous flow to represent SMC motility and matrix reorganization in response to graft adaptation. Our implementation is modular, and this makes us able to choose the right compromise between closeness to the physiological reality and complexity of the model. The focus of this paper is to offer a new modular implementation that combines the best features of an agent-based model, continuum mechanics, and particle-tracking methods to cope with the multiscale nature of the adaptation phenomena. This hybrid method allows us to quickly test various hypotheses with a particular attention to cellular motility, a process that we demonstrated should be driven by mechanical homeostasis in order to maintain the right balance between cells and extracellular matrix in order to reproduce a distribution similar to histological experimental data from vein grafts.

A Twofold Usage of an Agent-Based Model of Vascular Adaptation to Design Clinical Experiments.

Several computational models of Vein Graft Bypass (VGB) adaptation have been developed in order to improve the surgical outcome and they all share a common property: their accuracy relies on a winning choice of their driving coefficients which are best to be retrieved from experimental data. Since experiments are time-consuming and resources-demanding, the golden standard is to know in advance which measures need to be retrieved on the experimental table and out of how many samples. Accordingly, our goal is to build a computational framework able to pre-design an effective experimental structure to optimize the computational models setup. Our hypothesis is that an Agent-Based Model (ABM) developed by our group is comparable enough to a true set of experiments to be used to generate reliable virtual experimental data. Thanks to a twofold usage of our ABM, we created a filter to be posed before the real experiment in order to drive its optimal design. This work is the natural continuation of a previous study from our group [1], where the attention was posed on simple single-cellular events models. With this new version we focused on more complex models with the purpose of verifying that the complexity of the experimental setup grows proportionally with the accuracy of the model itself.

A Computational Model-Based Framework to Plan Clinical Experiments - an Application to Vascular Adaptation Biology.

Several computational models have been developed in order to improve the outcome of Vein Graft Bypasses in response to arterial occlusions and they all share a common property: their accuracy relies on a winning choice of the coefficients' value related to biological functions that drive them. Our goal is to optimize the retrieval of these unknown coefficients on the base of experimental data and accordingly, as biological experiments are noisy in terms of statistical analysis and the models are typically stochastic and complex, this work wants first to elucidate which experimental measurements might be sufficient to retrieve the targeted coefficients and second how many specimens would constitute a good dataset to guarantee a sufficient level of accuracy. Since experiments are often costly and time consuming, the planning stage is critical to the success of the operation and, on the base of this consideration, the present work shows how, thanks to an ad hoc use of a computational model of vascular adaptation, it is possible to estimate in advance the entity and the quantity of resources needed in order to efficiently reproduce the experimental reality.

A method for going from 2D laparoscope to 3D acquisition of surface landmarks by a novel computer vision approach.

PURPOSE: This paper presents a method to use the Smart Trocars-our new surgical instrument recognition system-or any accurate localization system of surgical instrument for acquiring intraoperative surface data. Complex laparoscopic surgeries need a proper guidance system which requires registering the preoperative data from a CT or MRI scan to the intraoperative patient state. The Smart Trocar can be used to localize the instruments when it comes to contact with the soft tissue surface. METHOD: Two successive views through the laparoscope at different angles with the 3D localization of a fixed tool at one single location using the Smart Trocars can point out visible features during the surgery and acquire their location in 3D to provide a depth map in the region of interest. In other words, our method transforms a standard laparoscope system into a system with three-dimensional registration capability. RESULT: This method was initially tested on a simulation for uncertainty assessment and then on a rigid model for verification with an accuracy within 2 mm distance. In addition, an in vivo experiment on pig model was also conducted to investigate how the method might be used during a physiologic respiratory cycle. CONCLUSION: This method can be applied in a large number of surgical applications as a guidance system on its own or in conjunction with other navigation techniques. Our work encourages further testing with realistic surgical applications in the near future.

Linking gene dynamics to vascular hyperplasia - Toward a predictive model of vein graft adaptation.

Reductionist approaches, where individual pieces of a process are examined in isolation, have been the mainstay of biomedical research. While these methods are effective in highly compartmentalized systems, they fail to account for the inherent plasticity and non-linearity within the signaling structure. In the current manuscript, we present the computational architecture for tracking an acute perturbation in a biologic system through a multiscale model that links gene dynamics to cell kinetics, with the overall goal of predicting tissue adaptation. Given the complexity of the genome, the problem is made tractable by clustering temporal changes in gene expression into unique patterns. These cluster elements form the core of an integrated network that serves as the driving force for the response of the biologic system. This modeling approach is illustrated using the clinical scenario of vein bypass graft adaptation. Vein segments placed in the arterial circulation for treatment of advanced occlusive disease can develop an aggressive hyperplastic response that narrows the lumen, reduces blood flow, and induces in situ thrombosis. Reducing this hyperplastic response has been a long-standing but unrealized goal of biologic researchers in the field. With repeated failures of single target therapies, the redundant response pathways are thought to be a fundamental issue preventing progress towards a solution. Using the current framework, we demonstrate how theoretical genomic manipulations can be introduced into the system to shift the adaptation to a more beneficial phenotype, where the hyperplastic response is mitigated and the risk of thrombosis reduced. Utilizing our previously published rabbit vein graft genomic data, where grafts were harvested at time points ranging from 2 hours to 28 days and under differential flow conditions, and a customized clustering algorithm, five gene clusters that differentiated the low flow (i.e., pro-hyperplastic) from high flow (i.e., anti-hyperplastic) response were identified. The current analysis advances these general associations to create a model that identifies those genes sets most likely to be of therapeutic benefit. Using this approach, we examine the range of potential opportunities for intervention via gene cluster over-expression or inhibition, delivered in isolation or combination, at the time of vein graft implantation.

Measuring Mental Workload with EEG+fNIRS.

We studied the capability of a Hybrid functional neuroimaging technique to quantify human mental workload (MWL). We have used electroencephalography (EEG) and functional near-infrared spectroscopy (fNIRS) as imaging modalities with 17 healthy subjects performing the letter n-back task, a standard experimental paradigm related to working memory (WM). The level of MWL was parametrically changed by variation of n from 0 to 3. Nineteen EEG channels were covering the whole-head and 19 fNIRS channels were located on the forehead to cover the most dominant brain region involved in WM. Grand block averaging of recorded signals revealed specific behaviors of oxygenated-hemoglobin level during changes in the level of MWL. A machine learning approach has been utilized for detection of the level of MWL. We extracted different features from EEG, fNIRS, and EEG+fNIRS signals as the biomarkers of MWL and fed them to a linear support vector machine (SVM) as train and test sets. These features were selected based on their sensitivity to the changes in the level of MWL according to the literature. We introduced a new category of features within fNIRS and EEG+fNIRS systems. In addition, the performance level of each feature category was systematically assessed. We also assessed the effect of number of features and window size in classification performance. SVM classifier used in order to discriminate between different combinations of cognitive states from binary- and multi-class states. In addition to the cross-validated performance level of the classifier other metrics such as sensitivity, specificity, and predictive values were calculated for a comprehensive assessment of the classification system. The Hybrid (EEG+fNIRS) system had an accuracy that was significantly higher than that of either EEG or fNIRS. Our results suggest that EEG+fNIRS features combined with a classifier are capable of robustly discriminating among various levels of MWL. Results suggest that EEG+fNIRS should be preferred to only EEG or fNIRS, in developing passive BCIs and other applications which need to monitor users' MWL.

Vascular Adaptation: Pattern Formation and Cross Validation between an Agent Based Model and a Dynamical System.

Myocardial infarction is the global leading cause of mortality (Go et al., 2014). Coronary artery occlusion is its main etiology and it is commonly treated by Coronary Artery Bypass Graft (CABG) surgery (Wilson et al, 2007). The long-term outcome remains unsatisfactory (Benedetto, 2016) as the graft faces the phenomenon of restenosis during the post-surgery, which consists of re-occlusion of the lumen and usually requires secondary intervention even within one year after the initial surgery (Harskamp, 2013). In this work, we propose an extensive study of the restenosis phenomenon by implementing two mathematical models previously developed by our group: a heuristic Dynamical System (DS) (Garbey and Berceli, 2013), and a stochastic Agent Based Model (ABM) (Garbey et al., 2015). With an extensive use of the ABM, we retrieved the pattern formations of the cellular events that mainly lead the restenosis, especially focusing on mitosis in intima, caused by alteration in shear stress, and mitosis in media, fostered by alteration in wall tension. A deep understanding of the elements at the base of the restenosis is indeed crucial in order to improve the final outcome of vein graft bypass. We also turned the ABM closer to the physiological reality by abating its original assumption of circumferential symmetry. This allowed us to finely replicate the trigger event of the restenosis, i.e. the loss of the endothelium in the early stage of the post-surgical follow up (Roubos et al., 1995) and to simulate the encroachment of the lumen in a fashion aligned with histological evidences (Owens et al., 2015). Finally, we cross-validated the two models by creating an accurate matching procedure. In this way we added the degree of accuracy given by the ABM to a simplified model (DS) that can serve as powerful predictive tool for the clinic.

A robust construction algorithm of the centerline skeleton for complex aortic vascular structure using computational fluid dynamics.

Centerlines of blood vessels are useful tools to make important anatomical measurements (length, diameter, area), which cannot be accurately obtained using 2D images. In this paper a brand new method for centerline extraction of vascular trees is presented. By using computational fluid dynamics (CFD) we are able to obtain a robust and purely functional centerline allowing us to support better measurements than classic purely geometrical-based centerlines. We show that the CFD-based centerline is within a few pixels from the geometrical centerline where the latter is defined (far away from inlet/outlets and from the branches). We show that the centerline computed with our method is not affected by traditional errors of other classical volume-based algorithms such as topological thinning, and could be a potential alternative to be considered for future studies.

The SmartOR: a distributed sensor network to improve operating room efficiency.

BACKGROUND: Despite the significant expense of OR time, best practice achieves only 70% efficiency. Compounding this problem is a lack of real-time data. Most current OR utilization programs require manual data entry. Automated systems require installation and maintenance of expensive tracking hardware throughout the institution. This study developed an inexpensive, automated OR utilization system and analyzed data from multiple operating rooms. STUDY DESIGN: OR activity was deconstructed into four room states. A sensor network was then developed to automatically capture these states using only three sensors, a local wireless network, and a data capture computer. Two systems were then installed into two ORs, recordings captured 24/7. The SmartOR recorded the following events: any room activity, patient entry/exit time, anesthesia time, laparoscopy time, room turnover time, and time of preoperative patient identification by the surgeon. RESULTS: From November 2014 to December 2015, data on 1003 cases were collected. The mean turnover time was 36 min, and 38% of cases met the institutional goal of ≤30 min. Data analysis also identified outlier cases (>1 SD from mean) in the domains of time from patient entry into the OR to intubation (11% of cases) and time from extubation to patient exiting the OR (11% of cases). Time from surgeon identification of patient to scheduled procedure start time was 11 min (institution bylaws require 20 min before scheduled start time), yet OR teams required 22 min on average to bring a patient into the room after surgeon identification. CONCLUSION: The SmartOR automatically and reliably captures data on OR room state and, in real time, identifies outlier cases that may be examined closer to improve efficiency. As no manual entry is required, the data are indisputable and allow OR teams to maintain a patient-centric focus.

Image-guided simulation of tissue deformation using a mechanical model on a surgical application.

This paper presents a method for localizing the position of a liver and a tumor within the tissue during a minimally invasive liver operation. From pre-operative CT scans, the liver volume and its internal structures are segmented using image-processing techniques. Based on these segmentations, a three-dimensional mechanical model is built to compute the liver volume and internal structure displacement under boundary conditions such as external forces from the surgical instrument. This can help the surgeon understand the motion of internal structures when manipulating the liver. To validate our method, an experiment on a porcine liver explant was performed to assess the difference between actual tissue motion and the mechanical model.

Esophageal stent migration: Testing few hypothesis with a simplified mathematical model.

Esophageal stent placement has significantly improved the quality of life in patients with malignant as well as benign esophageal obstructing lesions. Despite its early success and rapid adoption, stent migration still occurs in as many as 30% of cases especially with fully covered stents. To date, few models of interaction between the stent and the esophageal wall have been published and these have only focused on the deployment of the stent or the static mechanical stress distribution of the stent material. To elucidate the mechanism behind esophageal stent migration we developed a simplified radially symmetric computational model of esophageal peristalsis and the stent. A thorough review of the literature on esophageal peristalsis was performed and pertinent data were implemented into the model. Similarly, mechanical properties of an existing esophageal stent were used for the stent model. A sensitivity analysis of the parameters of the model enabled identification of the key elements of stent design that influence the degree of stent migration including flares design, stent length as well as longitudinal and radial stiffness. A comparison of the model to the migration rate reported in clinical studies for various types of fully covered stents further verified our model, which can significantly contribute to the development of a more stable esophageal stent with lower rates of migration.