# Geodesic B-Score for Improved Assessment of Knee Osteoarthritis

arXiv: https://arxiv.org/abs/2104.01107 · HTML: https://mlsanity.com/paper/2104.01107/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 3 of its 34 references that are in the map.
- First posted on arXiv 12 Mar 2021.

## Builds on (most cited first, 3 of 3)

- [Nonlinear Regression on Manifolds for Shape Analysis using Intrinsic B'ezier Splines](https://mlsanity.com/paper/2007.05275/) (2020)
- [An efficient Exact-PGA algorithm for constant curvature manifolds](https://mlsanity.com/paper/1603.03984/) (2016)
- [Towards Shape-based Knee Osteoarthritis Classification using Graph Convolutional Networks](https://mlsanity.com/paper/1910.06119/) (2019)

## Built on by (most cited first, 1 of 1)

- [Intrinsic shape analysis in archaeology: A case study on ancient sundials](https://mlsanity.com/paper/2305.18960/) (2023)

## Abstract (arXiv)

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.