A deep learning approach for projection and body-side classification in musculoskeletal radiographs.

ABSTRACT:

Background

The growing prevalence of musculoskeletal diseases increases radiologic workload, highlighting the need for optimized workflow management and automated metadata classification systems. We developed a large-scale, well-characterized dataset of musculoskeletal radiographs and trained deep learning neural networks to classify radiographic projection and body side.

Methods

In this IRB-approved retrospective single-center study, a dataset of musculoskeletal radiographs from 2011 to 2019 was retrieved and manually labeled for one of 45 possible radiographic projections and the depicted body side. Two classification networks were trained for the respective tasks using the Xception architecture with a custom network top and pretrained weights. Performance was evaluated on

SUBMITTER: Fink A 

PROVIDER: S-EPMC10866807 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature