ArticleFrontiers in medicine2022
Deep Learning-Based Classification of Inflammatory Arthritis by Identification of Joint Shape Patterns-How Neural Networks Can Tell Us Where to "Deep Dive" Clinically.
Article in Frontiers in medicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers, 1 of them a synthesis that pooled it.
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Who cites it
17 citing papers in PubMed, 1 synthesis or guideline pooled it, 30 citations in OpenAlex.
- Artificial intelligence in rheumatoid arthritis: current applications and future perspectives.Frontiers in medicine · 2026Pooled it
- Effect of apremilast on entheseal bone damage in psoriasis patients at risk of developing psoriatic arthritis - data from the prospective, interventional EPOS study.Arthritis research & therapy · 2026Article
- Demystifying machine learning approaches in digital bone imaging using microCT and HRpQCT.Bone reports · 2026Review
- The Use of Artificial Intelligence in Improving Diagnostic Modalities in Rheumatoid Arthritis: A Narrative Review.Cureus · 2026Review
- Artificial intelligence in the diagnosis and assessment of damage in peripheral inflammatory arthritis: a scoping review.Therapeutic advances in musculoskeletal disease · 2026Article
- Unconventional Imaging Methods in Psoriatic Arthritis.Current rheumatology reports · 2025Review
- Review
- 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
- Commentary: Targeting fibroblast activation protein in rheumatoid arthritis: from molecular imaging to precision therapeutics.Frontiers in immunology · 2025Article
- RA-XTNet: A Novel CNN Model to Predict Rheumatoid Arthritis from Hand Radiographs and Thermal Images: A Comparison with CNN Transformer and Quantum Computing.Diagnostics (Basel, Switzerland) · 2024Article
- Imaging in psoriatic arthritis: established methods and emerging techniques.Therapeutic advances in musculoskeletal disease · 2024Review
- [From conventional to cutting edge imaging in rheumatology].Zeitschrift fur Rheumatologie · 2023Review
- Review
- A Framework of Faster CRNN and VGG16-Enhanced Region Proposal Network for Detection and Grade Classification of Knee RA.Diagnostics (Basel, Switzerland) · 2023Article
- DeepNAPSI multi-reader nail psoriasis prediction using deep learning.Scientific reports · 2023Article
- Application of clinical and molecular profiling data to improve patient outcomes in psoriatic arthritis.Therapeutic advances in musculoskeletal disease · 2023Review
- A multicentre validation study of a smartphone application to screen hand arthritis.BMC musculoskeletal disorders · 2022Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Objective: We investigated whether a neural network based on the shape of joints can differentiate between rheumatoid arthritis (RA), psoriatic arthritis (PsA), and healthy controls (HC), which class patients with undifferentiated arthritis (UA) are assigned to, and whether this neural network is able to identify disease-specific regions in joints. Methods: We trained a novel neural network on 3D articular bone shapes of hand joints of RA and PsA patients as well as HC. Bone shapes were created from high-resolution peripheral-computed-tomography (HR-pQCT) data of the second metacarpal bone head. Heat maps of critical spots were generated using GradCAM. After training, we fed shape patterns of UA into the neural network to classify them into RA, PsA, or HC. Results: Hand bone shapes from 932 HR-pQCT scans of 617 patients were available. The network could differentiate the classes with an area-under-receiver-operator-curve of 82% for HC, 75% for RA, and 68% for PsA. Heat maps identified anatomical regions such as bare area or ligament attachments prone to erosions and bony spurs. When feeding UA data into the neural network, 86% were classified as "RA," 11% as "PsA," and 3% as "HC" based on the joint shape. Conclusion: We investigated neural networks to differentiate the shape of joints of RA, PsA, and HC and extracted disease-specific characteristics as heat maps on 3D joint shapes that can be utilized in clinical routine examination using ultrasound. Finally, unspecific diseases such as UA could be grouped using the trained network based on joint shape.
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