Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review

Abstract

Background: Artificial intelligence (AI) has the capacity to optimise the diagnosis and assessment of damage in inflammatory arthritis. The validation and performance metrics of AI algorithms should be interrogated in order to meaningfully map the existing data and identify evidence gaps. Objectives: To review the literature examining the use of AI algorithms to diagnose and assess damage in rheumatoid arthritis (RA) and psoriatic arthritis (PsA), using plain radiography (XR), ultrasound, computed tomography (CT) and magnetic resonance imaging (MRI). Design: A scoping review was performed for relevant English-language full-text articles or conference abstracts published prior to 12 September 2024 in the EMBASE, PubMed and Web of Science electronic databases. Methods: Abstract, full-text review and data extraction were completed by two authors independently utilising the Covidence platform. Data extracted included the study aims, publication type, study design, imaging modality, joints assessed, patient selection, diagnoses, algorithm(s) used, modifications and techniques utilised to improve model performance, testing and validation methodology and algorithm performance metrics. The review was completed in adherence to the Preferred Reporting Items for Systematic reviews and Meta-Analysis extension for Scoping Reviews (PRISMA-ScR) guidelines. Results: Two thousand and sixteen abstracts were screened following removal of 497 duplicates. Forty studies were included for data extraction, four of which were conference abstracts. A majority of studies were published in the last decade (88%) and a minority of studies involved patients with PsA (12.5%). Most studies utilised XR (n = 35), followed by MRI (n = 3), CT (n = 1) and USS (n = 1). Convolutional neural networks were the most commonly used algorithms. Only 15 studies (37.5%) used clearly described training, validation and testing datasets in algorithm development and testing. Conclusion: There is a paucity of data in the use of AI algorithms for the imaging assessment of PsA compared to RA. In RA and PsA, only a minority of published studies have utilised robust training, validation and testing methodologies in algorithm development. There was significant heterogeneity in the study populations, study design and performance metrics used, which limits comparisons between algorithms and the synthesis of results. Trial registration: Registration was not required in line with the PRISMA-ScR guidelines.