ArticlePediatric research2024
Artificial intelligence for nailfold capillaroscopy analyses - a proof of concept application in juvenile dermatomyositis.
Article in Pediatric research, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Treating juvenile dermatomyositis to target: Paediatric Rheumatology European Society/Childhood Arthritis and Rheumatology Research Alliance-endorsed recommendations from an international task force.Annals of the rheumatic diseases · 2025Guideline
- Nailfold capillary phenotypes distinguish juvenile myositis subtypes and associate with disease activity.Clinical and translational medicine · 2026Article
- Comparison of artificial intelligence and rheumatologists in nailfold capillaroscopic evaluation.Revista da Associacao Medica Brasileira (1992) · 2026Article
- Nailfold videocapillaroscopy in juvenile dermatomyositis: detailed correlations between microvascular abnormalities with clinical and laboratory parameters from an observational retrospective single-center study.Clinical rheumatology · 2025Observational
- Nailfold videocapillaroscopic changes in patients with psoriatic arthritis: A systematic review.Biomedical reports · 2025Article
- Closed circuit artificial ıntelligence model named morgaf for childhood onset systemic lupus erythematosus diagnosis.Scientific reports · 2025Article
- Expert Perspective: Diagnostic Approach to Differentiating Juvenile Dermatomyositis From Muscular Dystrophy.Arthritis & rheumatology (Hoboken, N.J.) · 2025Article
- Abnormality detection in nailfold capillary images using deep learning with EfficientNet and cascade transfer learning.Scientific reports · 2025Article
- The emerging paradigm in pediatric rheumatology: harnessing the power of artificial intelligence.Rheumatology international · 2024Review
- Reliability of a generative artificial intelligence tool for pediatric familial Mediterranean fever: insights from a multicentre expert survey.Pediatric rheumatology online journal · 2024Article
- What does artificial intelligence mean in rheumatology?Archives of rheumatology · 2024Review
- Automatic assessment of nailfold capillaroscopy software: a pilot study.Reumatologia · 2024Article
- Nailfold capillary density in 140 untreated children with juvenile dermatomyositis: an indicator of disease activity.Pediatric rheumatology online journal · 2023Article
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7 authors.
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No grant is acknowledged in the PubMed record.
Abstract
backgroundBiomarkers for idiopathic inflammatory myopathies are difficult to identify and may involve expensive laboratory tests. We assess the potential for artificial intelligence (AI) to differentiate children with juvenile dermatomyositis (JDM) from healthy controls using nailfold capillaroscopy (NFC) images. We also assessed the potential of NFC images to reflect the range of disease activity with JDM.
methodsA total of 1,120 NFC images from 111 children with active JDM, diagnosed between 1990 and 2020, and 321 NFC images from 31 healthy controls were retrieved from the CureJM JDM Registry. We built a lightweight and explainable deep neural network model called NFC-Net. Images were downscaled by interpolation techniques to reduce the computational cost.
resultsNFC-Net achieved high performance in differentiating patients with JDM from controls, with an area under the ROC curve (AUROC) of 0.93 (0.84, 0.99) and accuracy of 0.91 (0.82, 0.92). With sensitivity (0.85) and specificity (0.90) resulted in model precision of 0.95. The AUROC and accuracy for predicting clinical disease activity from inactivity were 0.75 (0.61, 0.81) and 0.74 (0.65, 0.79).
conclusionThe good performance of the NFC-Net demonstrates that NFC images are sufficient for detecting often unrecognized JDM disease activity, providing a reliable indicator of disease status. IMPACT: Proposed NFC-Net can accurately predict children with JDM from healthy controls using nailfold capillaroscopy (NFC) images. Additionally, it predicts the scores to JDM disease activity versus no activity. Equipped with gradients, NFC-Net is explainable and gives visual information beside the reported accuracies. NFC-Net is computationally efficient since it is applied to substantially downscaled NFC images. Furthermore, the model can be wrapped within an edge-based device like a mobile application that is accessible to both clinicians and patients.
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