A Correspondence-Based Network Approach for Groupwise Analysis of Patient-Specific Spatiotemporal Data.

ABSTRACT: Methods for statistically analyzing patient-specific data that vary both spatially and over time are currently either limited to summary statistics or require elaborate surface registration. We propose a new method, called correspondence-based network analysis, which leverages particle-based shape modeling to establish correspondence across a population and preserve patient-specific measurements and predictions through statistical analysis. Herein, we evaluated this method using three published datasets of the hip describing cortical bone thickness of the proximal femur, cartilage contact stress, and dynamic joint space between control and patient cohorts to evaluate activity- and group-based differences, as applicable, using traditional statistical parametric mapping (SPM) and our propose

SUBMITTER: Atkins PR 

PROVIDER: S-EPMC11047278 | biostudies-literature | 2023 Oct

REPOSITORIES: biostudies-literature