2025 ISAKOS Congress in Munich, Germany

2025 ISAKOS Biennial Congress Paper


Patellar Tilt Calculation Utilizing Artificial Intelligence on CT Knee Imaging

Johannes M. Sieberer, MSc, New Haven UNITED STATES
Nancy Park, BS, New Haven, CT UNITED STATES
Albert L Rancu, BS, New Haven, CT UNITED STATES
Kelsey Brennan, ., New Haven, CT UNITED STATES
Armita Razieh Manafzadeh, PhD, New Haven, CT UNITED STATES
Steven Tommasini, PhD, New Haven, Connecticut UNITED STATES
Daniel Wiznia, MD
John P. Fulkerson, MD, Litchfield, CT UNITED STATES

Yale University, New Haven, CT, UNITED STATES

FDA Status Not Applicable

Summary

The patellar tilt measurement can be automized using commerical and open source software.

Abstract

Background

In the diagnosis of patellar instability, three-dimensional (3D) imaging enables measurement of a wide range of metrics. However, measuring these metrics can be time-consuming and prone to error due to conducting 2D measurements on 3D objects. This study aims to measure patellar tilt in 3D and automate it by utilizing a commercial AI algorithm for landmark placement.

Methods

CT-scans of 30 patients with at least two dislocation events and 30 controls without patellofemoral disease were acquired. Patellar tilt was measured using three different methods: the established method, and by calculating the angle between 3D-landmarks placed by either a human rater or an AI algorithm. Correlations between the three measurements were calculated using interclass correlation coefficients, and differences with a Kruskall-Wallis test. Significant differences of means between patients and controls were calculated using Mann-Whitney U tests. Significance was assumed at 0.05 adjusted with the Bonferroni method.

Results

No significant differences (overall: p=0.10, patients: 0.51, controls: 0.79) between methods were found. Predicted ICC between the methods ranged from 0.86 to 0.90 with a 95% confidence interval of 0.77 to 0.94. Differences between patients and controls were significant (p<0.001) for all three methods.

Conclusion

The study offers an alternative 3D approach for calculating patellar tilt comparable to traditional, manual measurements. Furthermore, this analysis offers evidence that a commercially available software can identify the necessary anatomical landmarks for patellar tilt calculation, offering a potential pathway to increased automation of surgical decision-making metrics.

Keywords: Patellar tilt, Artificial Intelligence, Three-dimensional (3D)