# Tailored Multibody Tibiofemoral Joint Model for Precision Care

## TL;DR

This paper introduces a detailed knee joint model to simulate surgical outcomes and improve precision in knee treatments.

## Contribution

A patient-specific tibiofemoral joint model with 15 ligaments and realistic force interactions for surgical simulation.

## Key findings

- The model shows strong correlation with cadaver data for passive knee flexion (r > 0.89).
- Anterior–posterior translation had the highest correlation (R² = 0.994) with experimental data.
- Results are statistically significant (p < 0.05), validating the model's accuracy.

## Abstract

Knee motion involves intricate coordination among various anatomical structures. Effective treatment of knee pathologies requires precise identification of deformities and accurate surgical interventions, which often involve rapid tissue modification based on established knowledge. However, motion disorders are typically detected long after surgery. To address this, a simulation environment is proposed to plan and analyze surgical impacts on knee motion. Comprehensive knee joint modeling is crucial for a successful simulation. Clinically accepted movement procedures based on passive knee motion make tibiofemoral articulation modeling sufficient. Proposed model tibiofemoral articulation, incorporating 15 ligaments, tibial and femoral bones, and cartilages. Ligaments’ tensile, bones’, and cartilages’ contact forces (CFs) define internal force interactions. Anatomical structures, their shapes, positions, and attachment points are identified from MRI, ensuring patient‐specific modeling. Simulation results are compared to cadaver data using passive knee motion. Two rotational and three translational dependent joint motions (JMs) are compared pairwise. The results are highly correlated with the clinical benchmark. Pearson’s correlation show a strong association between experimental and simulated passive knee flexions (PKFs; r > 0.89). The comparison is statistically significant with p < 0.05. Anterior–posterior translation showed the highest correlation (R
2 = 0.994). The findings indicate that the simulated model closely replicates actual knee responses.