scholarly journals Bayesian model uncertainty quantification for hyperelastic soft tissue models

2021 ◽  
Vol 2 ◽  
Author(s):  
Milad Zeraatpisheh ◽  
Stephane P.A. Bordas ◽  
Lars A.A. Beex
Keyword(s):  
Parameter Uncertainty ◽  
Model Uncertainty ◽  
Bayesian Model ◽  
Soft Tissues ◽  
Patient Specific ◽  
Considerable Uncertainty ◽  
Surgical Simulations ◽  
Identification Approach ◽  
Tissue Models ◽  
Constitutive Parameters

Abstract Patient-specific surgical simulations require the patient-specific identification of the constitutive parameters. The sparsity of the experimental data and the substantial noise in the data (e.g., recovered during surgery) cause considerable uncertainty in the identification. In this exploratory work, parameter uncertainty for incompressible hyperelasticity, often used for soft tissues, is addressed by a probabilistic identification approach based on Bayesian inference. Our study particularly focuses on the uncertainty of the model: we investigate how the identified uncertainties of the constitutive parameters behave when different forms of model uncertainty are considered. The model uncertainty formulations range from uninformative ones to more accurate ones that incorporate more detailed extensions of incompressible hyperelasticity. The study shows that incorporating model uncertainty may improve the results, but this is not guaranteed.

scholarly journals Quantitative Assessment of 3D Printed Blood Vessels Produced with J750™ Digital Anatomy™ for Suture Simulation

2022 ◽  
Author(s):  
Stefania Marconi ◽  
Valeria Mauri ◽  
Erika Negrello ◽  
Luigi Pugliese ◽  
Andrea Pietrabissa ◽  
...  
Keyword(s):  
Blood Vessels ◽  
Soft Tissues ◽  
Surgical Simulation ◽  
Patient Specific ◽  
Morphological Aspects ◽  
Pathological Conditions ◽  
Surgical Simulations ◽  
Training Of Surgeons ◽  
3D Printed ◽  
Digital Anatomy

Blood vessels anastomosis is one of the most challenging and delicate tasks to learn in many surgical specialties, especially for vascular and abdominal surgeons. Such a critical skill implies a learning curve that goes beyond technical execution. The surgeon needs to gain proficiency in adapting gestures and the amount of force expressed according to the type of tissue he/she is dealing with. In this context, surgical simulation is gaining a pivotal role in the training of surgeons, but currently available simulators can provide only standard or simplified anatomies, without the chance of presenting specific pathological conditions and rare cases. 3D printing technology, allowing the manufacturing of extremely complex geometries, find a perfect application in the production of realistic replica of patient-specific anatomies. According to available technologies and materials, morphological aspects can be easily handled, while the reproduction of tissues mechanical properties still poses major problems, especially when dealing with soft tissues. The present work focuses on blood vessels, with the aim of identifying – by means of both qualitative and quantitative tests – materials combinations able to best mimic the behavior of the biological tissue during anastomoses, by means of J750™ Digital Anatomy™ technology and commercial photopolymers from Stratasys. Puncture tests and stitch traction tests are used to quantify the performance of the various formulations. Surgical simulations involving anastomoses are performed on selected clinical cases by surgeons to validate the results. A total of 37 experimental materials were tested and 2 formulations were identified as the most promising solutions to be used for anastomoses simulation. Clinical applicative tests, specifically selected to challenge the new materials, raised additional issues on the performance of the materials to be considered for future developments.

scholarly journals Automatic modelling of human musculoskeletal ligaments – Framework overview and model quality evaluation

2021 ◽  
pp. 1-14
Author(s):  
Noura Hamze ◽  
Lukas Nocker ◽  
Nikolaus Rauch ◽  
Markus Walzthöni ◽  
Matthias Harders ◽  
...  
Keyword(s):  
Statistical Model ◽  
Soft Tissues ◽  
Automatic Generation ◽  
Cadaveric Study ◽  
Patient Specific ◽  
Mean Square ◽  
Shape Modelling ◽  
Morphological Studies ◽  
Insertion Sites ◽  
Mean Square Errors

BACKGROUND: Accurate segmentation of connective soft tissues in medical images is very challenging, hampering the generation of geometric models for bio-mechanical computations. Alternatively, one could predict ligament insertion sites and then approximate the shapes, based on anatomical knowledge and morphological studies. OBJECTIVE: In this work, we describe an integrated framework for automatic modelling of human musculoskeletal ligaments. METHOD: We combine statistical shape modelling with geometric algorithms to automatically identify insertion sites, based on which geometric surface/volume meshes are created. As clinical use case, the framework has been applied to generate models of the forearm interosseous membrane. Ligament insertion sites in the statistical model were defined according to anatomical predictions following a published approach. RESULTS: For evaluation we compared the generated sites, as well as the ligament shapes, to data obtained from a cadaveric study, involving five forearms with 15 ligaments. Our framework permitted the creation of models approximating ligaments’ shapes with good fidelity. However, we found that the statistical model trained with the state-of-the-art prediction of the insertion sites was not always reliable. Average mean square errors as well as Hausdorff distances of the meshes could increase by an order of magnitude, as compared to employing known insertion locations of the cadaveric study. Using those, an average mean square error of 0.59 mm and an average Hausdorff distance of less than 7 mm resulted, for all ligaments. CONCLUSIONS: The presented approach for automatic generation of ligament shapes from insertion points appears to be feasible but the detection of the insertion sites with a SSM is too inaccurate, thus making a patient-specific approach necessary.

Counterfactual explanation of Bayesian model uncertainty

2021 ◽  
Author(s):  
Gohar Ali ◽  
Feras Al-Obeidat ◽  
Abdallah Tubaishat ◽  
Tehseen Zia ◽  
Muhammad Ilyas ◽  
...  
Keyword(s):  
Model Uncertainty ◽  
Bayesian Model

scholarly journals Intermittent Hormone Therapy Models Analysis and Bayesian Model Comparison for Prostate Cancer

2021 ◽  
Vol 84 (1) ◽  
Author(s):  
S. Pasetto ◽  
H. Enderling ◽  
R. A. Gatenby ◽  
R. Brady-Nicholls
Keyword(s):  
Prostate Cancer ◽  
Bayesian Model ◽  
Model Comparison ◽  
Locally Advanced ◽  
Basin Of Attraction ◽  
Specific Antigen ◽  
Resistance Mechanisms ◽  
Patient Specific ◽  
Response Dynamics ◽  
Bayesian Model Comparison

AbstractThe prostate is an exocrine gland of the male reproductive system dependent on androgens (testosterone and dihydrotestosterone) for development and maintenance. First-line therapy for prostate cancer includes androgen deprivation therapy (ADT), depriving both the normal and malignant prostate cells of androgens required for proliferation and survival. A significant problem with continuous ADT at the maximum tolerable dose is the insurgence of cancer cell resistance. In recent years, intermittent ADT has been proposed as an alternative to continuous ADT, limiting toxicities and delaying time-to-progression. Several mathematical models with different biological resistance mechanisms have been considered to simulate intermittent ADT response dynamics. We present a comparison between 13 of these intermittent dynamical models and assess their ability to describe prostate-specific antigen (PSA) dynamics. The models are calibrated to longitudinal PSA data from the Canadian Prospective Phase II Trial of intermittent ADT for locally advanced prostate cancer. We perform Bayesian inference and model analysis over the models’ space of parameters on- and off-treatment to determine each model’s strength and weakness in describing the patient-specific PSA dynamics. Additionally, we carry out a classical Bayesian model comparison on the models’ evidence to determine the models with the highest likelihood to simulate the clinically observed dynamics. Our analysis identifies several models with critical abilities to disentangle between relapsing and not relapsing patients, together with parameter intervals where the critical points’ basin of attraction might be exploited for clinical purposes. Finally, within the Bayesian model comparison framework, we identify the most compelling models in the description of the clinical data.

scholarly journals Bayesian Model Averaging to Account for Model Uncertainty in Estimates of a Vaccine's Effectiveness

2021 ◽  
Author(s):  
Carlos R Oliveira ◽  
Eugene D Shapiro ◽  
Daniel M Weinberger
Keyword(s):  
Model Selection ◽  
Model Uncertainty ◽  
Bayesian Model ◽  
Bayesian Model Averaging ◽  
Model Averaging ◽  
Selection Methods ◽  
Final Model ◽  
Negative Case ◽  
Confounder Selection ◽  
Control Study

Vaccine effectiveness (VE) studies are often conducted after the introduction of new vaccines to ensure they provide protection in real-world settings. Although susceptible to confounding, the test-negative case-control study design is the most efficient method to assess VE post-licensure. Control of confounding is often needed during the analyses, which is most efficiently done through multivariable modeling. When a large number of potential confounders are being considered, it can be challenging to know which variables need to be included in the final model. This paper highlights the importance of considering model uncertainty by re-analyzing a Lyme VE study using several confounder selection methods. We propose an intuitive Bayesian Model Averaging (BMA) framework for this task and compare the performance of BMA to that of traditional single-best-model-selection methods. We demonstrate how BMA can be advantageous in situations when there is uncertainty about model selection by systematically considering alternative models and increasing transparency.

scholarly journals Efficient finite element hyperelasticity solver for blood vessel simulations

2020 ◽  
Vol 14 ◽  
pp. 174830262093101
Author(s):  
Xinhong Wang ◽  
Zhengzheng Yan ◽  
Yi Jiang ◽  
Rongliang Chen
Keyword(s):  
Finite Element ◽  
Blood Vessel ◽  
Blood Vessels ◽  
High Performance ◽  
Soft Tissues ◽  
Circulatory System ◽  
Numerical Algorithms ◽  
External Pressure ◽  
Patient Specific ◽  
Schwarz Method

The blood vessels play a key role in the human circulatory system. As a tremendous amount of efforts have been devoted to develop mathematical models for investigating the elastic behaviors of human blood vessels, high performance numerical algorithms aiming at solving these models have attracted attention. In this work, we present an efficient finite element solver for an elastodynamic model which is commonly used for simulating soft tissues under external pressure loadings. In particular, the elastic material is assumed to satisfy the Saint–Venant–Kirchhoff law, the governing equation is spatially discretized by a finite element method, and a fully implicit backward difference method is used for the temporal discretization. The resulting nonlinear system is then solved by a Newton–Krylov–Schwarz method. It is the first time to apply the Newton–Krylov–Schwarz method to the Saint–Venant–Kirchhoff model for a patient-specific blood vessel. Numerical tests verify the efficiency of the proposed method and demonstrate its capability for bioengineering applications.

Compact Two Degrees-of-Freedom External Fixator System for Correction of Persistent Clubfoot Deformity

2019 ◽  
Vol 13 (2) ◽  
Author(s):  
Ying Ying Wu ◽  
Anton Plakseychuk ◽  
Kenji Shimada
Keyword(s):  
External Fixator ◽  
Degrees Of Freedom ◽  
Soft Tissues ◽  
Three Dimensional ◽  
Deformity Correction ◽  
Current Method ◽  
Tissue Growth ◽  
Learning Curves ◽  
Patient Specific ◽  
Two Degrees Of Freedom

Bone deformities are often complex three-dimensional (3D) deformities, and correcting them is difficult. To correct persistent clubfoot deformity in adolescents or adults, an external fixator is sometimes used to encourage tissue growth and preserve healthy tissues. However, it is difficult to set up, resulting in long surgeries and steep learning curves for surgeons. It is also bulky and obstructs patient mobility. In this paper, we introduce a new approach of defining clubfoot deformity correction as a six degrees-of-freedom (6DOF) correction, and then reducing it to just two degrees-of-freedom (2DOF) using the axis-angle representation. Therefore, only two physical trajectory joints are needed, which in turn enables a more compact fixator design. A computer planner was developed to minimize the bulk of the external fixator, and to optimize the distraction schedule to avoid overstretching the soft tissues. This reduces the learning curve for surgeons and shortens surgery time. To validate the system, a patient-specific clubfoot simulator was developed, and four experiments were performed on the clubfoot simulator. The accuracy of midfoot correction was 11 mm and 3.5 deg without loading, and 41 mm and 11.7 deg with loading. While the external fixator has to be more rigid to overcome resistance against correction, the surgical system itself was able to achieve accurate correction in less than 2 h. This is an improvement from the current method, which takes 2.5–4.5 h.

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