ArticleDiagnostics (Basel, Switzerland)2021
Method for Diagnosing the Bone Marrow Edema of Sacroiliac Joint in Patients with Axial Spondyloarthritis Using Magnetic Resonance Image Analysis Based on Deep Learning.
Article in Diagnostics (Basel, Switzerland), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers, 2 of them syntheses that pooled it.
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Who cites it
22 citing papers in PubMed, 2 syntheses or guidelines pooled it, 42 citations in OpenAlex.
- The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review.European radiology · 2025Pooled it
- Performances of machine learning algorithms in discriminating sacroiliitis features on MRI: a systematic review.RMD open · 2023Pooled it
- Can radiomics replace the SPARCC scoring system in evaluating bone marrow edema of sacroiliac joints in patients with axial spondyloarthritis?Clinical rheumatology · 2023Trial
- Evaluating the Performance of ChatGPT-4V in Detecting Inflammatory Magnetic Resonance Imaging Findings of Sacroiliitis: Potentials, Challenges, and Limitations.Journal of imaging informatics in medicine · 2026Article
- Early diagnosis of axial spondyloarthritis in primary care using multi-agent systems.NPJ digital medicine · 2026Article
- Article
- Deep learning detects retropharyngeal edema on MRI in patients with acute neck infections.European radiology experimental · 2025Article
- Deep learning for automated grading of radiographic sacroiliitis.Quantitative imaging in medicine and surgery · 2025Article
- Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human.Journal of rheumatic diseases · 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
- A deep neural network for MRI spinal inflammation in axial spondyloarthritis.European spine journal : official publication of the European Spine Society, the European Spinal Deformity Society, and the European Section of the Cervical Spine Research Society · 2024Article
- Bone marrow edema detection for diagnostic support of axial spondyloarthritis using MRI.International journal of computer assisted radiology and surgery · 2024Article
- Artificial intelligence improves the diagnosis of human leukocyte antigen (HLA)-B27-negative axial spondyloarthritis based on multi-sequence magnetic resonance imaging and clinical features.Quantitative imaging in medicine and surgery · 2024Article
- Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning.Insights into imaging · 2024Article
- Deep learning in rheumatological image interpretation.Nature reviews. Rheumatology · 2024Review
- The Development and Validation of an AI Diagnostic Model for Sacroiliitis: A Deep-Learning Approach.Diagnostics (Basel, Switzerland) · 2023Article
- [Multi-sequence relation attention network for diagnosing HLA-B27-negative axial spondyloarthritis].Nan fang yi ke da xue xue bao = Journal of Southern Medical University · 2023Article
- DCNAS-Net: deformation convolution and neural architecture search detection network for bone marrow oedema.BMC medical imaging · 2023Article
- Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis.Frontiers in immunology · 2023Article
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Authors and funding
5 authors at 3 institutions in 1 country.
Funding
Abstract
Axial spondyloarthritis (axSpA) is a chronic inflammatory disease of the sacroiliac joints. In this study, we develop a method for detecting bone marrow edema by magnetic resonance (MR) imaging of the sacroiliac joints and a deep-learning network. A total of 815 MR images of the sacroiliac joints were obtained from 60 patients diagnosed with axSpA and 19 healthy subjects. Gadolinium-enhanced fat-suppressed T1-weighted oblique coronal images were used for deep learning. Active sacroiliitis was defined as bone marrow edema, and the following processes were performed: setting the region of interest (ROI) and normalizing it to a size suitable for input to a deep-learning network, determining bone marrow edema using a convolutional-neural-network-based deep-learning network for individual MR images, and determining sacroiliac arthritis in subject examinations based on the classification results of individual MR images. About 70% of the patients and normal subjects were randomly selected for the training dataset, and the remaining 30% formed the test dataset. This process was repeated five times to calculate the average classification rate of the five-fold sets. The gradient-weighted class activation mapping method was used to validate the classification results. In the performance analysis of the ResNet18-based classification network for individual MR images, use of the ROI showed excellent detection performance of bone marrow edema with 93.55 ± 2.19% accuracy, 92.87 ± 1.27% recall, and 94.69 ± 3.03% precision. The overall performance was additionally improved using a median filter to reflect the context information. Finally, active sacroiliitis was diagnosed in individual subjects with 96.06 ± 2.83% accuracy, 100% recall, and 94.84 ± 3.73% precision. This is a pilot study to diagnose bone marrow edema by deep learning based on MR images, and the results suggest that MR analysis using deep learning can be a useful complementary means for clinicians to diagnose bone marrow edema.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.