autoscoRA: Deep Learning to Automate Sharp/van der Heijde Scoring of Radiographic Damage in Rheumatoid Arthritis

Abstract

Abstract Objective Regular imaging by conventional radiography to assess for joint damage is a cornerstone in the management of rheumatoid arthritis (RA). Scoring systems to quantify such damage, such as the widely used Sharp/van der Heijde (SvdH) score, are limited by the requirement of time and experienced staff as well as intra- and inter-rater variability. To alleviate these problems, autoscoRA, a fully automated scoring system to assign SvdH scores to radiographs of the hands and feet was developed. Methods Using the hitherto largest dataset of adult rheumatoid arthritis patients, autoscoRA, a deep learning-based system, was trained to automatically perform joint extraction and scoring of joint space narrowing and bone erosion. Results The dataset included 769 patients (155 of which in the test set) with 3437 visits (707) and 12144 radiographs (2507). The model reached excellent agreement with a human scorer for joint space narrowing, erosion, and combined scores both on the joint level and for summed total SvdH scores (ICC 0.9). On a subset of data scored by a second human reader, the model outperformed the former in terms of agreement with the first human reader. In addition, autoscoRA demonstrated good agreement with a human reader for detecting longitudinal progression of joint damage across different SvdH score cut-offs defining the presence of progression (average agreement of 70 %). Conclusion Automated systems like autoscoRA could be used to facilitate scoring of radiographic joint damage in clinical trials, registries and observational studies, and, eventually, routine clinical care.

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