ARTSS, a deep learning pipeline for automated Sharp/van der Heijde rheumatoid arthritis scoring from hand X-rays, reports MAE 0.95 and 99% joint detection, but its key results table contains a mathematically impossible RMSE/MAE pair.
Rheumatoid Arthritis: Automated Scoring of Radiographic Joint Damage
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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.
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Automated Radiographic Total Sharp Score (ARTSS) in Rheumatoid Arthritis: A Solution to Reduce Inter-Intra Reader Variation and Enhancing Clinical Practice
ARTSS, a deep learning pipeline for automated Sharp/van der Heijde rheumatoid arthritis scoring from hand X-rays, reports MAE 0.95 and 99% joint detection, but its key results table contains a mathematically impossible RMSE/MAE pair.