Rheumatoid Arthritis: Automated Scoring of Radiographic Joint Damage

TL;DR

This paper presents a deep learning pipeline that automatically scores rheumatoid arthritis joint damage from radiographs, providing rapid, objective assessments that could improve clinical decision-making.

Contribution

The study introduces a novel deep learning-based method for automatic, accurate scoring of rheumatoid arthritis joint damage from radiographs, reducing reliance on expert judgment.

Findings

High balanced accuracy in scoring results

Rapid assessment within a few minutes

Reduction of subjectivity in scoring

Abstract

Rheumatoid arthritis is an autoimmune disease that causes joint damage due to inflammation in the soft tissue lining the joints known as the synovium. It is vital to identify joint damage as soon as possible to provide necessary treatment early and prevent further damage to the bone structures. Radiographs are often used to assess the extent of the joint damage. Currently, the scoring of joint damage from the radiograph takes expertise, effort, and time. Joint damage associated with rheumatoid arthritis is also not quantitated in clinical practice and subjective descriptors are used. In this work, we describe a pipeline of deep learning models to automatically identify and score rheumatoid arthritic joint damage from a radiographic image. Our automatic tool was shown to produce scores with extremely high balanced accuracy within a couple of minutes and utilizing this would remove the subjectivity of the scores between human reviewers.