Geodesic B-Score for Improved Assessment of Knee Osteoarthritis
This work addresses the need for reader-independent, personalized assessment of knee osteoarthritis, offering an incremental improvement over existing methods.
The authors tackled the problem of automatically assessing knee osteoarthritis from 3D medical images by generalizing the B-score to Riemannian shape spaces, resulting in improved discrimination ability over Euclidean methods, as demonstrated by better predictive validity for total knee replacement risks.
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.