A two-step neural network-based guiding system for obtaining reliable radiographs for critical shoulder angle measurement.

ABSTRACT:

Background

The critical shoulder angle (CSA) has been reported to be highly associated with rotator cuff tears (RCTs) and an increased risk of RCT re-tears. However, the measurement of the CSA is greatly affected by the malpositioning of the shoulder. To address this issue, a two-step neural network-based guiding system was developed to obtain reliable CSA radiographs, and its feasibility and accuracy was evaluated.

Methods

A total of 1,754 shoulder anteroposterior (AP) radiographs were retrospectively acquired to train and validate a two-step neural network-based guiding system to obtain reliable CSA radiographs. The study included patients aged 18 years or older who underwent X-rays and/or computed tomography (CT) scans of the shoulder. Patients who had undergone shoulder

SUBMITTER: Alike Y 

PROVIDER: S-EPMC10895144 | biostudies-literature | 2024 Feb

REPOSITORIES: biostudies-literature