A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging

Bordner, Adrien; Aouad, Théodore; López-Medina, Clementina; Sisi, Yang,; Moltó, Anna; Talbot, Hugues; Dougados, Maxime; Feydy, A.

doi:10.1016/j.diii.2023.03.008

Cited by 12 publications

(7 citation statements)

References 22 publications

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“…The purpose of this review was to reveal the current state of AI integration in MRI for identifying inflammatory changes in RA and axSpA. The findings indicate that AI significantly contributes to the early detection of synovitis, BME, and bone erosions in these two rheumatic diseases, as supported by various studies ( 62 , 63 , 71 , 75 ).…”

Section: Discussionmentioning

confidence: 91%

“…Bordner et al ( 71 ) created a DL model, named region-based convolutional neural network (mask-RCNN), designed to detect BME and predict the presence of active sacroiliitis on MRI based on the ASAS criteria (requiring BME to be identified in at least two different locations in a single slice). The model’s diagnostic efficacy in predicting active sacroiliitis according to ASAS criteria was assessed using sensitivity, specificity, Matthews correlation coefficient (MCC), accuracy and AUC.…”

Section: Applications Of Artificial Intelligence Based On Mri In Axia...mentioning

confidence: 99%

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Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Nicoara,

Sas,

Bita

et al. 2023

Front. Med.

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Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.

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Section: Discussionmentioning

confidence: 91%

Section: Applications Of Artificial Intelligence Based On Mri In Axia...mentioning

confidence: 99%

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Nicoara,

Sas,

Bita

et al. 2023

Front. Med.

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“…Previous studies developed DL models to detect different types of inflammation or structural damage of axSpA, with AUCs of 0.76–0.98 for bone marrow edema on SIJ MRI [ 37 , 38 ], 0.92 for erosion, and 0.91 for ankylosis on SIJ CT [ 15 ]. Our experimental results demonstrated the effective application of DL methods in FM, filling a critical gap in the automatic identification of SIJ changes reflecting axSpA progression (i.e., inflammation-erosion-FM-new bone formation) [ 39 ].…”

Section: Discussionmentioning

confidence: 99%

“…Our experimental results demonstrated the effective application of DL methods in FM, filling a critical gap in the automatic identification of SIJ changes reflecting axSpA progression (i.e., inflammation-erosion-FM-new bone formation) [ 39 ]. High value of DL models has been confirmed in identifying inflammation and structural damage indicative of axSpA based on SIJ image analysis [ 38 , 40 ]. However, the previous researchers did not independently analyze FM.…”

Section: Discussionmentioning

confidence: 99%

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

Li,

Lin,

Xie

et al. 2024

Insights Imaging

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Objective To develop a deep learning (DL) model for segmenting fat metaplasia (FM) on sacroiliac joint (SIJ) MRI and further develop a DL model for classifying axial spondyloarthritis (axSpA) and non-axSpA. Materials and methods This study retrospectively collected 706 patients with FM who underwent SIJ MRI from center 1 (462 axSpA and 186 non-axSpA) and center 2 (37 axSpA and 21 non-axSpA). Patients from center 1 were divided into the training, validation, and internal test sets (n = 455, 64, and 129). Patients from center 2 were used as the external test set. We developed a UNet-based model to segment FM. Based on segmentation results, a classification model was built to distinguish axSpA and non-axSpA. Dice Similarity Coefficients (DSC) and area under the curve (AUC) were used for model evaluation. Radiologists’ performance without and with model assistance was compared to assess the clinical utility of the models. Results Our segmentation model achieved satisfactory DSC of 81.86% ± 1.55% and 85.44% ± 6.09% on the internal cross-validation and external test sets. The classification model yielded AUCs of 0.876 (95% CI: 0.811–0.942) and 0.799 (95% CI: 0.696–0.902) on the internal and external test sets, respectively. With model assistance, segmentation performance was improved for the radiological resident (DSC, 75.70% vs. 82.87%, p < 0.05) and expert radiologist (DSC, 85.03% vs. 85.74%, p > 0.05). Conclusions DL is a novel method for automatic and accurate segmentation of FM on SIJ MRI and can effectively increase radiologist’s performance, which might assist in improving diagnosis and progression of axSpA. Critical relevance statement DL models allowed automatic and accurate segmentation of FM on sacroiliac joint MRI, which might facilitate quantitative analysis of FM and have the potential to improve diagnosis and prognosis of axSpA. Key points • Deep learning was used for automatic segmentation of fat metaplasia on MRI. • UNet-based models achieved automatic and accurate segmentation of fat metaplasia. • Automatic segmentation facilitates quantitative analysis of fat metaplasia to improve diagnosis and prognosis of axial spondyloarthritis. Graphical Abstract

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“…[ 25 ] Studies have evaluated AI-based models to automatically identify bone marrow edema in MRI images of sacroiliitis and its significance with respect to the diagnosis of spondyloarthritis. [ 26 ] The potential for the use of deep learning to analyze radiological images is enormous, potentially helping to diagnose and evaluate healing or progression (on serial images) or synovitis, joint erosions, marrow edema, and cartilage architecture. [ 2 ] Similarly, the analysis of lung CTs using deep learning for the diagnosis and evolution of interstitial lung disease or pulmonary pathology in diseases such as systemic sclerosis, RA, anti-neutrophil cytoplasmic antibody-associated vasculitis, and sarcoidosis are areas for exploration.…”

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confidence: 99%

What does artificial intelligence mean in rheumatology?

Chandwar,

Prasanna Misra

2024

Arch Rheumatol

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Intelligence is the ability of humans to learn from experiences to ascribe conscious weights and unconscious biases to modulate their outputs from given inputs. Transferring this ability to computers is artificial intelligence (AI). The ability of computers to understand data in an intelligent manner is machine learning. When such learning is with images and videos, which involves deeper layers of artificial neural networks, it is described as deep learning. Large language models are the latest development in AI which incorporate self-learning into deep learning through transformers. AI in Rheumatology has immense potential to revolutionize healthcare and research. Machine learning could aid clinical diagnosis and decision-making, and deep learning could extend this to analyze images of radiology or positron emission tomography scans or histopathology images to aid a clinician’s diagnosis. Analysis of routinely obtained patient data or continuously collected information from wearables could predict disease flares. Analysis of high-volume genomics, transcriptomics, proteomics, or metabolomics data from patients could help identify novel markers of disease prognosis. AI might identify newer therapeutic targets based on in-silico modelling of omics data. AI could help automate medical administrative work such as inputting information into electronic health records or transcribing clinic notes. AI could help automate patient education and counselling. Beyond the clinic, AI has the potential to aid medical education. The ever-expanding capabilities of AI models bring along with them considerable ethical challenges, particularly related to risks of misuse. Nevertheless, the widespread use of AI in Rheumatology is inevitable and a progress with great potential.

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A deep learning model for the diagnosis of sacroiliitis according to Assessment of SpondyloArthritis International Society classification criteria with magnetic resonance imaging

Cited by 12 publications

References 22 publications

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Automatic segmentation of fat metaplasia on sacroiliac joint MRI using deep learning

What does artificial intelligence mean in rheumatology?

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