Automated assessment of synovitis in 0.2 T magnetic resonance images of the wrist

Czaplicka, Kamila; Wojciechowski, Wadim; Włodarczyk, Justyna; Urbanik, Andrzej; Tabor, Zbisław

doi:10.1016/j.compbiomed.2015.10.009

Cited by 8 publications

(6 citation statements)

References 16 publications

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“…The correlation coefficient between computer‐aided SV and the RAMRIS or manual measurements was reported in five studies (140 MRIs; Supplementary Figure 6 ; 24 , 31 , 35 , 36 , 37 ). Pooled analysis demonstrated an overall moderate level of agreement between (semi)automated and manual assessment of SV, with the weighted pooled ICC of 0.82 (95% CI: 0.75‐0.87; P < 0.001; I 2 = 0%; Table 2 ).…”

Section: Resultsmentioning

confidence: 99%

“…The intra‐ and inter‐reader reliability of the RAMRIS and/or manual assessment of synovitis was reported in five studies (156 MRIs; 24 , 31 , 35 , 36 , 37 ). Pooled analysis showed that intrareader ( P = 0.208; pooled ICC of 0.94 [95% CI: 0.84‐0.98] for manual measurements) and inter‐reader reliability ( P = 0.782; pooled ICC of 0.85 [95% CI: 0.75‐0.91] for manual measurements) of the RAMRIS and/or manual SV measurement was not significantly different from (semi)automated SV measurement.…”

Section: Resultsmentioning

confidence: 99%

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Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

Haj‐Mirzaian

Kubassova

Boesen

et al. 2022

ACR Open Rheumatology

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Objective To summarize the feasibility of computer‐assisted quantification of joint pathologies on magnetic resonance imaging (MRI) in patients with inflammatory arthritis by evaluating the published data on reliability, validity, and feasibility. Methods A systematic literature search was performed for original articles published from January 1, 1985, to January 1, 2021. We selected studies in which patients with inflammatory arthritis were enrolled, and arthritis‐related structural damage/synovitis in peripheral joints was assessed on non‐contrast‐enhanced, contrast‐enhanced (CE), or dynamic CE (DCE)‐MRI using (semi)automated methods. Data were pooled using random‐effects model. Results Twenty‐eight studies consisting of 1342 MRIs were included (mean age, 54.8 years; 66.7% female; duration of arthritis, 3.6 years). Among clinical/laboratory factors, synovial membrane volume (SV) was moderately correlated with erthrocyte sedimentation rate (ESR) level ( P < 0.01). Pooled analysis showed an overall excellent intra‐ and inter‐reader reliability for computer‐aided quantification of bone erosion volume (BEV; r = 0.97 [95% CI: 0.92‐0.99], 0.93 [0.87‐0.97]), SV (r = 0.98 [95% CI: 0.90‐0.99], 0.86 [0.78‐0.91]), and DCE‐MRI perfusion parameters (r = 0.96‐0.99). Meta‐regression showed that computer‐aided and manual methods provide comparable reliability ( P > 0.05). Computer‐aided measurement of BEV (r = 0.92), SV (r = 0.82), and DCE‐MRI biomarkers (r = 0.72 N‐total; r = 0.74 N‐plateau; r = 0.64 N‐washout) were significantly correlated with the Rheumatoid Arthritis Magnetic Resonance Imaging Score (RAMRIS; P < 0.01), allowing for earlier assessment of drug efficacy. On average, (semi)automated analysis of BEV/SV took 17 minutes (vs. 9 minutes for the RAMRIS) and DCE‐MRI took 4 minutes (vs. 33 minutes for manual assessment). Conclusion Computer‐aided image quantification technologies demonstrate excellent reliability and validity when used to quantify MRI pathologies of peripheral joints in patients with inflammatory arthritis. Computer‐aided evaluation of inflammatory arthritis is an emerging field and should be considered as a viable complement to conventional observer‐based scoring methods for clinical trials application.

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

Haj‐Mirzaian

Kubassova

Boesen

et al. 2022

ACR Open Rheumatology

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“…A different automated model, created by Czaplicka et al ( 66 ), focused on evaluating synovitis through MRI utilizing pre- and post-contrast T1W sequences in RA patients. The study revealed a robust correlation between the automatically measured volume and the manually calculated synovitis volume, along with the RAMRIS scores.…”

Section: Applications Of Artificial Intelligence Based On Mri In Rheu...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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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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“…Machine learning-based algorithms, both supervised and unsupervised, have been developed to detect and quantify synovitis using MRI images [ 71 , 76 ]. Computer-aided diagnostic approaches have been highly consistent with manual synovitis quantifications in dynamic-contrast enhanced (DCE) MRI, while they can significantly reduce the time spent by the observer reading the image [ 76 , 77 ]. We did not find any DL-based study assessing synovitis on wrist MRI.…”

Section: Artificial Intelligence In Ramentioning

confidence: 99%

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

2022

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Investigation of the potential applications of artificial intelligence (AI), including machine learning (ML) and deep learning (DL) techniques, is an exponentially growing field in medicine and healthcare. These methods can be critical in providing high-quality care to patients with chronic rheumatological diseases lacking an optimal treatment, like rheumatoid arthritis (RA), which is the second most prevalent autoimmune disease. Herein, following reviewing the basic concepts of AI, we summarize the advances in its applications in RA clinical practice and research. We provide directions for future investigations in this field after reviewing the current knowledge gaps and technical and ethical challenges in applying AI. Automated models have been largely used to improve RA diagnosis since the early 2000s, and they have used a wide variety of techniques, e.g., support vector machine, random forest, and artificial neural networks. AI algorithms can facilitate screening and identification of susceptible groups, diagnosis using omics, imaging, clinical, and sensor data, patient detection within electronic health record (EHR), i.e., phenotyping, treatment response assessment, monitoring disease course, determining prognosis, novel drug discovery, and enhancing basic science research. They can also aid in risk assessment for incidence of comorbidities, e.g., cardiovascular diseases, in patients with RA. However, the proposed models may vary significantly in their performance and reliability. Despite the promising results achieved by AI models in enhancing early diagnosis and management of patients with RA, they are not fully ready to be incorporated into clinical practice. Future investigations are required to ensure development of reliable and generalizable algorithms while they carefully look for any potential source of bias or misconduct. We showed that a growing body of evidence supports the potential role of AI in revolutionizing screening, diagnosis, and management of Sara Momtazmanesh and Ali Nowroozi have contributed equally to this work and share first authorship.

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Automated assessment of synovitis in 0.2 T magnetic resonance images of the wrist

Cited by 8 publications

References 16 publications

Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

Computer‐Assisted Image Analysis in Assessment of Peripheral Joint MRI in Inflammatory Arthritis: A Systematic Review and Meta‐analysis

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

Artificial Intelligence in Rheumatoid Arthritis: Current Status and Future Perspectives: A State-of-the-Art Review

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