A multi-scene deep learning model for automated segmentation of acute vertebral compression fractures from radiographs: a multicenter cohort study.

ABSTRACT:

Objective

To develop a multi-scene model that can automatically segment acute vertebral compression fractures (VCFs) from spine radiographs.

Methods

In this multicenter study, we collected radiographs from five hospitals (Hospitals A-E) between November 2016 and October 2019. The study included participants with acute VCFs, as well as healthy controls. For the development of the Positioning and Focus Network (PFNet), we used a training dataset consisting of 1071 participants from Hospitals A and B. The validation dataset included 458 participants from Hospitals A and B, whereas external test datasets 1-3 included 301 participants from Hospital C, 223 from Hospital D, and 261 from Hospital E, respectively. We evaluated the segmentation performance of the PFNet model and compa

SUBMITTER: Zhang H 

PROVIDER: S-EPMC11612108 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature