Assessment of joint space is a fundamental radiographic task to diagnose RA and to assess disease severity and progression. The clinical assessment of joint space is a visual task, and the current scoring systems, such as the modified Sharp’s score, generally follow the semiquantitative grading. Inter- and intra-observer variation impede the development of computer-aided detection and diagnosis software to help physicians.
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The researchers collected 1,397 conventional hand radiographs from 450 RA patients (309 training radiographs and 141 validation radiographs) whose diagnoses were based on the 2010 ACR/EULAR classification criteria for RA. Three rheumatologists assessed the joint space independently according to the van der Heijde-modified Sharp scoring method. The consensus was the ground truth for training data.
The researchers then trained a model to detect joints automatically using the Deep Adaptive Graph.
The short answer to the question is, yes, deep learning models are useful to detect specific joint area and score the joint space with performance comparable with rheumatologists.
The data presented in this session exceeded the scope of this article. We encourage readers to review the abstracts for details.
Keri Losavio is the editor of The Rheumatologist.
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