This article discusses the methodology and findings gained from efforts to diagnose patellar instability through three-dimensional imaging that enables measurement of a wide range of metrics; it offers evidence that a commercially available software can identify the necessary anatomical landmarks for patellar tilt calculation and offer a potential method for increasing automation of surgical decision-making metrics.
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. 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 Kruskal-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. 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 percent confidence interval of 0.77–0.94. Differences between patients and controls were significant (p < 0.001) for all three methods. 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. (Published Abstract Provided)