Deep learning model for measurement of shoulder critical angle and acromion index on shoulder radiographs.

ABSTRACT:

Background

Several bone morphological parameters, including the anterior acromion morphology, the lateral acromial angle, the coracohumeral interval, the glenoid inclination, the acromion index (AI), and the shoulder critical angle (CSA), have been proposed to impact the development of rotator cuff tears and glenohumeral osteoarthritis. This study aimed to develop a deep learning tool to automate the measurement of CSA and AI on anteroposterior shoulder radiographs.

Methods

We used MURA Dataset v1.1, which is a large publicly available musculoskeletal radiograph dataset from the Stanford University School of Medicine. All normal shoulder anteroposterior radiographs were extracted and annotated by an experienced orthopedic surgeon. The annotated images were divided into train

SUBMITTER: Shariatnia MM 

PROVIDER: S-EPMC10426517 | biostudies-literature | 2022 Aug

REPOSITORIES: biostudies-literature