Anatomy-centred deep learning improves generalisability and progression prediction in radiographic sacroiliitis detection.

ABSTRACT:

Purpose

To examine whether incorporating anatomy-centred deep learning can improve generalisability and enable prediction of disease progression.

Methods

This retrospective multicentre study included conventional pelvic radiographs of four different patient cohorts focusing on axial spondyloarthritis collected at university and community hospitals. The first cohort, which consisted of 1483 radiographs, was split into training (n=1261) and validation (n=222) sets. The other cohorts comprising 436, 340 and 163 patients, respectively, were used as independent test datasets. For the second cohort, follow-up data of 311 patients was used to examine progression prediction capabilities. Two neural networks were trained, one on images cropped to the bounding box of the sacroiliac jo

SUBMITTER: Dorfner FJ 

PROVIDER: S-EPMC11751840 | biostudies-literature | 2024 Dec

REPOSITORIES: biostudies-literature