Analysis of uncertainty and variability in finite element computational models for biomedical engineering: characterization and propagation

Computational modeling has become a powerful tool in biomedical engineering thanks to its potential to simulate coupled systems. However, real parameters are usually not accurately known, and variability is inherent in living organisms. To cope with this, probabilistic tools, statistical analysis and stochastic approaches have been used. This article aims to review the analysis of uncertainty and variability in the context of finite element modeling in biomedical engineering. Characterization techniques and propagation methods are presented, as well as examples of their applications in biomedical finite element simulations. Uncertainty propagation methods, both non-intrusive and intrusive, are described. Finally, pros and cons of the different approaches and their use in the scientific community are presented. This leads us to identify future directions for research and methodological development of uncertainty modeling in biomedical engineering

Patient-Specific Virtual Insertion of Electrode Array for Electrical Simulations of Cochlear Implants

International audienceSensorineural hearing loss is becoming one the most common reasons of disability. Worldwide 278 million people (around 25% of people above 45 years) suffer from moderate to several hearing disorders. Cochlear implantation (CI) enables to convert sound to an electrical signal that directly stimulates the auditory nerves via the electrode array surgically placed. However, this technique is intrinsically patient-dependent and its range of outcomes is very broad. A major source of outcome variability resides in the electrode array insertion. It has been reported to be one of the most important steps in cochlear implant surgery. In this context, we propose a method for patient-specific virtual electrode insertion further used into a finite element electrical simulation, and consequently improving the planning of the surgical implantation. The anatomical parameters involved in the electrode insertion such as the curvature and the number of turns of the cochlea, make virtual insertion highly challenging. Moreover, the influence of the insertion parameters and the use of different manufactured electrode arrays increase the range of scenarios to be considered for the implantation of a given patient. To this end, the method we propose is fast, easily parameterizable and applicable to a wide range of anatomies and insertion configurations. Our method is novel for targeting automatic virtual electrode insertion. Also, it combines high-resolution imaging techniques and clinical data to be further used into a finite element study and predict implantation outcomes in humans

Computational evaluation of cochlear implant surgery outcomes accounting for uncertainty and parameter variability

Cochlear implantation (CI) is a complex surgical procedure that restores hearing in patients with severe deafness. The successful outcome of the implanted device relies on a group of factors, some of them unpredictable or difficult to control. Uncertainties on the electrode array position and the electrical properties of the bone make it difficult to accurately compute the current propagation delivered by the implant and the resulting neural activation. In this context, we use uncertainty quantification methods to explore how these uncertainties propagate through all the stages of CI computational simulations. To this end, we employ an automatic framework, encompassing from the finite element generation of CI models to the assessment of the neural response induced by the implant stimulation. To estimate the confidence intervals of the simulated neural response, we propose two approaches. First, we encode the variability of the cochlear morphology among the population through a statistical shape model. This allows us to generate a population of virtual patients using Monte Carlo sampling and to assign to each of them a set of parameter values according to a statistical distribution. The framework is implemented and parallelized in a High Throughput Computing environment that enables to maximize the available computing resources. Secondly, we perform a patient-specific study to evaluate the computed neural response to seek the optimal post-implantation stimulus levels. Considering a single cochlear morphology, the uncertainty in tissue electrical resistivity and surgical insertion parameters is propagated using the Probabilistic Collocation method, which reduces the number of samples to evaluate. Results show that bone resistivity has the highest influence on CI outcomes. In conjunction with the variability of the cochlear length, worst outcomes are obtained for small cochleae with high resistivity values. However, the effect of the surgical insertion length on the CI outcomes could not be clearly observed, since its impact may be concealed by the other considered parameters. Whereas the Monte Carlo approach implies a high computational cost, Probabilistic Collocation presents a suitable trade-off between precision and computational time. Results suggest that the proposed framework has a great potential to help in both surgical planning decisions and in the audiological setting process

Automatic Generation of a Computational Model for Monopolar Stimulation of Cochlear Implants

International audienceCochlear implants have the potential to significantly improve severe sensorineural hearing loss. However, the outcome of this technique is highly variable and depends on patient-specific factors. We previously proposed a method for patient-specific electrical simulation after CI, which can assist in surgical planning of the CI and determination of the electrical stimulation pattern. However, the virtual implant placement and mesh generation were carried out manually and the process was not easily applied automatically for further cochlear anatomies. Moreover, in order to optimize the implant designs, it is important to develop a way to stimulate the results of the implantation in a population of virtual patients. In this work we propose an automatic framework for patient-specific electrical simulation in CI surgery. To the best of our knowledge, this is the first method proposed for patient-specific generation of hearing models which combines high-resolution imaging techniques, clinical CT data and virtual electrode insertion. Furthermore, we show that it is possible to use the computational models of virtual patients to simulate the results of the electrical activation of the implant in the cochlea and surrounding bone. This is an important step because it allows us to advance towards a complete surgical planning and implant optimization procedure

Non-invasive ventilation in obesity hypoventilation syndrome without severe obstructive sleep apnoea

Background Non-invasive ventilation (NIV) is an effective form of treatment in patients with obesity hypoventilation syndrome (OHS) who have concomitant severe obstructive sleep apnoea (OSA). However, there is a paucity of evidence on the efficacy of NIV in patients with OHS without severe OSA. We performed a multicentre randomised clinical trial to determine the comparative efficacy of NIV versus lifestyle modification (control group) using daytime arterial carbon dioxide tension (PaCO2) as the main outcome measure. Methods Between May 2009 and December 2014 we sequentially screened patients with OHS without severe OSA. Participants were randomised to NIV versus lifestyle modification and were followed for 2 months. Arterial blood gas parameters, clinical symptoms, health-related quality of life assessments, polysomnography, spirometry, 6-min walk distance test, blood pressure measurements and healthcare resource utilisation were evaluated. Statistical analysis was performed using intention-to-treat analysis. Results A total of 365 patients were screened of whom 58 were excluded. Severe OSA was present in 221 and the remaining 86 patients without severe OSA were randomised. NIV led to a significantly larger improvement in PaCO2 of -6 (95% CI -7.7 to -4.2) mm Hg versus -2.8 (95% CI -4.3 to -1.3) mm Hg, (p<0.001) and serum bicarbonate of -3.4 (95% CI -4.5 to -2.3) versus -1 (95% CI -1.7 to -0.2 95% CI) mmol/L (p<0.001). PaCO2 change adjusted for NIV compliance did not further improve the inter-group statistical significance. Sleepiness, some health-related quality of life assessments and polysomnographic parameters improved significantly more with NIV than with lifestyle modification. Additionally, there was a tendency towards lower healthcare resource utilisation in the NIV group. Conclusions NIV is more effective than lifestyle modification in improving daytime PaCO2, sleepiness and polysomnographic parameters. Long-term prospective studies are necessary to determine whether NIV reduces healthcare resource utilisation, cardiovascular events and mortality

Predictors of 1-year compliance with adaptive servoventilation in patients with heart failure and sleep disordered breathing: preliminary data from the ADVENT-HF trial

Despite its effectiveness in suppressing sleep disordered breathing (SDB), positive airway pressure therapy (PAP) is not always well tolerated by patients and long-term adherence can be problematic. Recently, two multicentre, randomised clinical trials (RCTs) tested the effects of PAP for patients with cardiovascular disease and co-existing SDB on morbidity and mortality with negative outcomes [1, 2]. Relatively poor adherence to PAP therapy (mean 3.7 and 3.3 h·day-1, respectively) in these two trials might have contributed to their poor results. Indeed, higher PAP use per day is associated with better clinical outcomes than lower use [3]