Geodesic B-Score for Improved Assessment of Knee Osteoarthritis

TL;DR

This paper introduces a geodesic B-score based on Riemannian shape spaces for more reliable, automatic assessment of knee osteoarthritis, demonstrating improved predictive validity over Euclidean methods for clinical decision-making.

Contribution

It generalizes the B-score to Riemannian shape spaces and provides an efficient algorithm for large-scale analysis, enhancing osteoarthritis assessment accuracy.

Findings

Improved discrimination ability over Euclidean B-score.

Demonstrated predictive validity for knee replacement risk.

Efficient computation for large shape populations.

Abstract

Three-dimensional medical imaging enables detailed understanding of osteoarthritis structural status. However, there remains a vast need for automatic, thus, reader-independent measures that provide reliable assessment of subject-specific clinical outcomes. To this end, we derive a consistent generalization of the recently proposed B-score to Riemannian shape spaces. We further present an algorithmic treatment yielding simple, yet efficient computations allowing for analysis of large shape populations with several thousand samples. Our intrinsic formulation exhibits improved discrimination ability over its Euclidean counterpart, which we demonstrate for predictive validity on assessing risks of total knee replacement. This result highlights the potential of the geodesic B-score to enable improved personalized assessment and stratification for interventions.