American Society of Biomechanics Journal of Biomechanics Award 2022: Computer models do not accurately predict human muscle passive muscle force and fiber length: Evaluating subject-specific modeling impact on musculoskeletal model predictions

Musculoskeletal models are valuable for studying and understanding the human body in a variety of clinical applications that include surgical planning, injury prevention, and prosthetic design. Subject-specific models have proven to be more accurate and useful compared to generic models. Nevertheless, it is important to validate all models when possible. To this end, gracilis muscle-tendon parameters were directly measured intraoperatively and used to test model predictions. The aim of this study was to evaluate the benefits and limitations of systematically incorporating subject-specific variables into muscle models used to predict passive force and fiber length. The results showed that incorporating subject-specific values generally reduced errors, although significant errors still existed. Optimization of the modeling parameter "tendon slack length" was explored in two cases: minimizing fiber length error and minimizing passive force error. The results showed that using all subject-specific values yielded the most favorable outcome in both models and optimization cases. However, the trade-off between fiber length error and passive force error will depend on the specific circumstances and research objectives due to significant individual errors. Notably, individual fiber length and passive force errors were as high as 20% and 37% respectively. Finally, the modeling parameter "tendon slack length" did not correlate with any real-world anatomical length.

Measuring and modeling in vivo human gracilis muscle-tendon unit length

Musculoskeletal models rely heavily on the use of an anatomical dataset and clearly defined assumptions to accurately model the subject being studied. Therefore, it is important to understand the limitations of using musculoskeletal models to study individuals. This paper describes a method of measuring in vivo gracilis muscle-tendon unit length and presents a comparison of experimental data versus predictions from four musculoskeletal models in OpenSim. The largest errors occurred when the knee was fully extended. At this position, the absolute average muscle-tendon unit length error was 7% and the absolute average fiber length error was between 15% and 32%. However, the variability of these errors was significant. Manual linear scaling based on an anthropometric database did not capture the variability observed in subjects. The fiber length errors observed are predicted to have a significant impact on muscle force production that may not represent true subject specific force-length relationship of the gracilis.

A procedure for determining subject-specific pulse oxygen saturation response

The oxyhemoglobin dissociation curve describes the relationship between the partial pressure of oxygen and the percent of hemoglobin saturated with oxygen and varies with chemical and physical factors that differ for every patient. If variability could be determined, patient-specific oxygen therapy could be administered. We have developed a procedure for characterizing variations in the oxygen dissociation curve. The purpose of this study was to validate this procedure in surgical patients. The procedure uses an automated system to alter oxygen therapy during surgery, within safe operational levels, and fit to Hill's equation non-invasive measurements of end-tidal oxygen and peripheral pulse oxygen saturation. The best-fit parameters for the Hill equation, estimated by iterative least squares, provide an apparent dissociation curve, meaningful of the patient-specific pulse oximeter response. Thirty-nine patients participated in this study. Using patient-specific parameter values increases correlation when compared with standard values. The procedure improved the model fit of patient saturation values significantly in 19 patients. This paper has demonstrated a procedure for determining patient-specific pulse oximeter response. This procedure determined best-fit parameters resulting in a significantly improved fit when compared with standard values. These best-fit parameters increased the coefficient of determination R² in all cases. Graphical Abstract This patient-specific procedure improves fit significantly compared to standard estimates.

Image-based modeling of acute myocardial ischemia using experimentally derived ischemic zone source representations

BACKGROUND

Computational models of myocardial ischemia often use oversimplified ischemic source representations to simulate epicardial potentials. The purpose of this study was to explore the influence of biophysically justified, subject-specific ischemic zone representations on epicardial potentials.

METHODS

We developed and implemented an image-based simulation pipeline, using intramural recordings from a canine experimental model to define subject-specific ischemic regions within the heart. Static epicardial potential distributions, reflective of ST segment deviations, were simulated and validated against measured epicardial recordings.

RESULTS

Simulated epicardial potential distributions showed strong statistical correlation and visual agreement with measured epicardial potentials. Additionally, we identified and described in what way border zone parameters influence epicardial potential distributions during the ST segment.

CONCLUSION

From image-based simulations of myocardial ischemia, we generated subject-specific ischemic sources that accurately replicated epicardial potential distributions. Such models are essential in understanding the underlying mechanisms of the bioelectric fields that arise during ischemia and are the basis for more sophisticated simulations of body surface ECGs.

Patient-specific bone modeling and analysis: the role of integration and automation in clinical adoption

Patient-specific analysis of bones is considered an important tool for diagnosis and treatment of skeletal diseases and for clinical research aimed at understanding the etiology of skeletal diseases and the effects of different types of treatment on their progress. In this article, we discuss how integration of several important components enables accurate and cost-effective patient-specific bone analysis, focusing primarily on patient-specific finite element (FE) modeling of bones. First, the different components are briefly reviewed. Then, two important aspects of patient-specific FE modeling, namely integration of modeling components and automation of modeling approaches, are discussed. We conclude with a section on validation of patient-specific modeling results, possible applications of patient-specific modeling procedures, current limitations of the modeling approaches, and possible areas for future research.