Deciphering the "Art" in Modeling and Simulation of the Knee Joint: Overall Strategy

Abstract

Recent explorations of knee biomechanics have benefited from computational modeling, specifically leveraging advancements in finite element analysis and rigid body dynamics of joint and tissue mechanics. A large number of models have emerged with different levels of fidelity in anatomical and mechanical representation. Adapted modeling and simulation processes vary widely, based on justifiable choices in relation to anticipated use of the model. However, there are situations where modelers' decisions seem to be subjective, arbitrary, and difficult to rationalize. Regardless of the basis, these decisions form the "art" of modeling, which impact the conclusions of simulation-based studies on knee function. These decisions may also hinder the reproducibility of models and simulations, impeding their broader use in areas such as clinical decision making and personalized medicine. This document summarizes an ongoing project that aims to capture the modeling and simulation workflow in its entirety-operation procedures, deviations, models, by-products of modeling, simulation results, and comparative evaluations of case studies and applications. The ultimate goal of the project is to delineate the art of a cohort of knee modeling teams through a publicly accessible, transparent approach and begin to unravel the complex array of factors that may lead to a lack of reproducibility. This manuscript outlines our approach along with progress made so far. Potential implications on reproducibility, on science, engineering, and training of modeling and simulation, on modeling standards, and on regulatory affairs are also noted.

Fig. 1

Number of publications focusing on knee modeling or simulation reaches up to 1000 per year. Total number of publications (up to year 2018) is 10,895 (data from PubMed2). With the increased fidelity of simulation software and computing hardware, use of finite element analysis in computational knee mechanics has gained traction. Annual number of studies are approaching 100 (total up to year 2018 is 882).

Fig. 2

Fundamental abstraction of modeling in knee biomechanics. Required input parameters include anatomical (geometry, mesh, etc.) and mechanical representations (stiffness, material properties, etc.) of joint components (bones, cartilage, ligaments, menisci, muscles, etc.), and loading and boundary conditions (external loads, muscular forces, etc.). Simulations look for predictions of mechanical response, e.g., joint movements, tissue stresses, and strains.

Fig. 3

Even visually, computational models of the knee joint exhibit large variations in anatomical and mechanical representations of tissue structures. Shown are samples of work by teams collaborating in a comprehensive study to understand the art of modeling and simulation in knee biomechanics: (a) open knee(s)—generation 1 from Cleveland Clinic team (Reproduced from [7]), (b) a model from the group at University of Denver (Reproduced from [8]), (c) work by researchers at Auckland Bioengineering Institute, (d) a current model from Cleveland State University, and (e) recent modeling by the team at Hospital for Special Surgery (Reproduced from [9]).

Fig. 4

Starting with the same data sets, each modeling and simulation (M & S) team goes through a sequence of modeling and simulation phases to come up with their own flavor of models representing the knee specimens of the data sets. The overarching goal of this study is to understand if the decisions of the modeling teams influence simulation predictions, and their interpretation to reach scientific and clinically relevant conclusions. Dissemination of all modeling and simulation outcomes and documentation of the whole lifecycle of the models will provide the opportunity to understand the source of variations in modeling decisions and the motivations behind them.

Fig. 5

Sample images from proposed model development specifications in regard to segmentation of ligaments: (a) the team from the University of Denver proposes to segment the posterior cruciate ligament using the paint tool in Simpleware ScanIP [63]. The segmentation will be used to determine insertion locations of springs, which will be refined using probed point data and (b) the team from the Cleveland Clinic proposes to use 3D Slicer [61] to manually segment the same ligament in order to generate a full continuum representation of its volume. It is interesting to note that both groups independently and unknowingly chose the same ligament approximately at the same image location to provide an example of ligament segmentation. Image from University of Denver documentation was cropped to match the bounds of the image from Cleveland Clinic documentation.