# Reproducibility in simulation-based prediction of natural knee mechanics

> **NIH NIH R01** · CLEVELAND CLINIC LERNER COM-CWRU · 2020 · $625,387

## Abstract

Computational modeling have become a routine and powerful strategy for academic research and clinical care.
Consequently, significant scientific discoveries were made, innovative products were launched, and
individualized delivery of healthcare has become a possibility. The scientific and clinical domain of knee
biomechanics is no exception. The knee is a major site of orthopaedic problems resulting in annual physician
visits on the order of tens of millions. Modeling and simulation offers a cost-effective and prompt path to
respond to the pressing medical needs for restoration of knee function. However, the reproducibility of
simulation results, to inform scientific and clinical decision making, is questionable. Reproducibility is a
pressing issue in scientific conduct. For modeling and simulation, there is added scrutiny particularly with the
desire to repurpose and reuse virtual specimens for prospective solutions of diverse scientific and clinical
problems. A significant portion of modeling and simulation workflow includes model development, evaluation,
and simulation. This workflow, while based on objective scientific principles, commonly requires intuition during
implementation; therefore relies on the knowledge and expertise of the modeler. This ‘art of modeling’ can be a
fundamental source of diminished reproducibility. The goal of this study is to understand how modelers’
choices to build models, even when using the same data, may influence predictions and therefore the
reproducibility of simulation results. Five modeling and simulation teams will independently develop
computational models of knees based on the same data sets and simulate the same scenarios, which are
relevant to scientific and clinical understanding of knee biomechanics. Ideally, predicted joint and tissue
mechanics will be the same. In practice, the skills and experiences of model developers will reflect upon their
modeling choices; and as a result, discrepancies will exist. The proposed activity will document the magnitude
and potential sources of such discrepancies through comparisons of model components and simulation results.
This project will examine and critique the current state of model development and simulation reproducibility in
joint and tissue mechanics. This will translate into reliable models of the knee joint for simulation-based
discoveries and in silico design and evaluation of medical devices and interventions. The required exchange of
data, model components, and simulation results among the teams and with the public will also impact
developers and users of such resources. Specifications, to facilitate data and model exchange and to develop
data and modeling standards, and guidance, to inform modeling and simulation workflows, will likely emerge as
by-products of the research activity. The investigators are leaders in simulation-based explorations of knee
biomechanics and include representatives of prominent research laboratories and clinical institu...

## Key facts

- **NIH application ID:** 10004617
- **Project number:** 5R01EB024573-04
- **Recipient organization:** CLEVELAND CLINIC LERNER COM-CWRU
- **Principal Investigator:** AHMET ERDEMIR
- **Activity code:** R01 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $625,387
- **Award type:** 5
- **Project period:** 2017-09-21 → 2023-06-30

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10004617

## Citation

> US National Institutes of Health, RePORTER application 10004617, Reproducibility in simulation-based prediction of natural knee mechanics (5R01EB024573-04). Retrieved via AI Analytics 2026-05-25 from https://api.ai-analytics.org/grant/nih/10004617. Licensed CC0.

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