Automatic evaluation of atlantoaxial subluxation in rheumatoid arthritis by a deep learning model.

ABSTRACT:

Background

This work aims to develop a deep learning model, assessing atlantoaxial subluxation (AAS) in rheumatoid arthritis (RA), which can often be ambiguous in clinical practice.

Methods

We collected 4691 X-ray images of the cervical spine of the 906 patients with RA. Among these images, 3480 were used for training the deep learning model, 803 were used for validating the model during the training process, and the remaining 408 were used for testing the performance of the trained model. The two-dimensional key points' detection model of Deep High-Resolution Representation Learning for Human Pose Estimation was adopted as the base convolutional neural network model. The model inferred four coordinates to calculate the atlantodental interval (ADI) and space available for th

SUBMITTER: Okita Y 

PROVIDER: S-EPMC10518918 | biostudies-literature | 2023 Sep

REPOSITORIES: biostudies-literature