ArticleRMD open2024
Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis.
Article in RMD open, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers, 1 of them a synthesis that pooled it.
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Who cites it
12 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review on Artificial Intelligence in Rheumatology Practice: From Implementation Concerns to Imaging and Clinical Application.International journal of rheumatic diseases · 2026Pooled it
- Artificial Intelligence and Machine Learning in Rheumatology and Systemic Inflammatory Diseases: From Pattern Recognition to Signal Analysis and Clinical Decision Support.Journal of clinical medicine · 2026Review
- Artificial Intelligence in Rheumatology: From Algorithms to Clinical Impact in Osteoporosis and Chronic Inflammatory Rheumatic Diseases.Journal of clinical medicine · 2026Article
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- Web of Science-Based Visualization of Mapping Global Scientific Production on Epstein-Barr Virus-Associated Autoimmune Diseases: A Bibliometric Study.Health science reports · 2025Article
- Automatic joint inflammation estimation based on regression neural networks.Medical physics · 2025Article
- Deep Learning Based Automatic Ankle Tenosynovitis Quantification from MRI in Patients with Psoriatic Arthritis: A Feasibility Study.Diagnostics (Basel, Switzerland) · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 2025Review
- Digital Health for Equitable Rheumatic Care: Integrating Real-World Experiences to Guide Policy Pathways.Healthcare (Basel, Switzerland) · 2025Review
- Unconventional Imaging Methods in Psoriatic Arthritis.Current rheumatology reports · 2025Review
- Current application, possibilities, and challenges of artificial intelligence in the management of rheumatoid arthritis, axial spondyloarthritis, and psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2025Review
- AI for imaging evaluation in rheumatology: applications of radiomics and computer vision-current status, future prospects and potential challenges.Rheumatology advances in practice · 2025Review
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Authors and funding
15 authors.
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Abstract
objectivesTo train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.
methodsHand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.
resultsIn total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset.
conclusionsWe developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.
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