Probabilistic planning for ligament-balanced TKA-Identification of critical ligament properties.

Total knee arthroplasty (TKA) failures are often attributed to unbalanced knee ligament loading. The current study aims to develop a probabilistic planning process to optimize implant component positioning that achieves a ligament-balanced TKA. This planning process accounts for both subject-specific uncertainty, in terms of ligament material properties and attachment sites, and surgical precision related to the TKA process typically used in clinical practice. The consequent uncertainty in the implant position parameters is quantified by means of a surrogate model in combination with a Monte Carlo simulation. The samples for the Monte Carlo simulation are generated through Bayesian parameter estimation on the native knee model in such a way that each sample is physiologically relevant. In this way, a subject-specific uncertainty is accounted for. A sensitivity analysis, using the delta-moment-independent sensitivity measure, is performed to identify the most critical ligament parameters. The designed process is capable of estimating the precision with which the targeted ligament-balanced TKA can be realized and converting this into a success probability. This study shows that without additional subject-specific information (e.g., knee kinematic measurements), a global success probability of only 12% is estimated. Furthermore, accurate measurement of reference strains and attachment sites critically improves the success probability of the pre-operative planning process. To allow more precise planning, more accurate identification of these ligament properties is required. This study underlines the relevance of investigating in vivo or intraoperative measurement techniques to minimize uncertainty in ligament-balanced pre-operative planning results, particularly prioritizing the measurement of ligament reference strains and attachment sites.

A Machine Learning Approach to Investigate the Uncertainty of Tissue-Level Injury Metrics for Cerebral Contusion.

Controlled cortical impact (CCI) on porcine brain is often utilized to investigate the pathophysiology and functional outcome of focal traumatic brain injury (TBI), such as cerebral contusion (CC). Using a finite element (FE) model of the porcine brain, the localized brain strain and strain rate resulting from CCI can be computed and compared to the experimentally assessed cortical lesion. This way, tissue-level injury metrics and corresponding thresholds specific for CC can be established. However, the variability and uncertainty associated with the CCI experimental parameters contribute to the uncertainty of the provoked cortical lesion and, in turn, of the predicted injury metrics. Uncertainty quantification via probabilistic methods (Monte Carlo simulation, MCS) requires a large number of FE simulations, which results in a time-consuming process. Following the recent success of machine learning (ML) in TBI biomechanical modeling, we developed an artificial neural network as surrogate of the FE porcine brain model to predict the brain strain and the strain rate in a computationally efficient way. We assessed the effect of several experimental and modeling parameters on four FE-derived CC injury metrics (maximum principal strain, maximum principal strain rate, product of maximum principal strain and strain rate, and maximum shear strain). Next, we compared the in silico brain mechanical response with cortical damage data from in vivo CCI experiments on pig brains to evaluate the predictive performance of the CC injury metrics. Our ML surrogate was capable of rapidly predicting the outcome of the FE porcine brain undergoing CCI. The now computationally efficient MCS showed that depth and velocity of indentation were the most influential parameters for the strain and the strain rate-based injury metrics, respectively. The sensitivity analysis and comparison with the cortical damage experimental data indicate a better performance of maximum principal strain and maximum shear strain as tissue-level injury metrics for CC. These results provide guidelines to optimize the design of CCI tests and bring new insights to the understanding of the mechanical response of brain tissue to focal traumatic brain injury. Our findings also highlight the potential of using ML for computationally efficient TBI biomechanics investigations.

Computationally Efficient Optimization Method to Quantify the Required Surgical Accuracy for a Ligament Balanced TKA.

OBJECTIVE: This study proposes a computationally efficient method to quantify the effect of surgical inaccuracies on ligament strain in total knee arthroplasty (TKA). More specifically, this study describes a framework to determine the implant position and required surgical accuracy that results in a ligament balanced post-operative outcome with a probability of 90%. METHODS: The response surface method is used to translate uncertainty in the implant position parameters to uncertainty in the ligament strain. The designed uncertainty quantification technique allows for an optimization with feasible computational cost towards the planned implant position and the tolerated surgical error for each of the twelve degrees of freedom of the implant position. RESULTS: It is shown that the error does not allow for a ligament balanced TKA with a probability of 90% using preoperative planning. Six critical implant position parameters can be identified, namely AP translation, PD translation, VV rotation, IE rotation for the femoral component and PD translation, VV rotation for the tibial component. CONCLUSION: We introduced an optimization process that allows for the computation of the required surgical accuracy for a ligament balanced postoperative outcome using preoperative planning with feasible computational cost. SIGNIFICANCE: Towards the research society, the proposed method allows for a computationally efficient uncertainty quantification on a complex model. Towards surgical technique developers, six critical implant position parameters were identified, which should be the focus when refining surgical accuracy of TKA, leveraging better patient satisfaction.