Development and validation of an artificial intelligence model to accurately predict spinopelvic parameters.

ABSTRACT:

Objective

Achieving appropriate spinopelvic alignment has been shown to be associated with improved clinical symptoms. However, measurement of spinopelvic radiographic parameters is time-intensive and interobserver reliability is a concern. Automated measurement tools have the promise of rapid and consistent measurements, but existing tools are still limited to some degree by manual user-entry requirements. This study presents a novel artificial intelligence (AI) tool called SpinePose that automatically predicts spinopelvic parameters with high accuracy without the need for manual entry.

Methods

SpinePose was trained and validated on 761 sagittal whole-spine radiographs to predict the sagittal vertical axis (SVA), pelvic tilt (PT), pelvic incidence (PI), sacral slope (SS), l

SUBMITTER: Harake ES 

PROVIDER: S-EPMC11494712 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature