Automatic quantification of tenosynovitis on MRI of the wrist in patients with early arthritis: a feasibility study

Aizenberg, Evgeni; Shamonin, Denis P; Reijnierse, M.; Mil, Annette H M van der Helm-van; Stoel, Berend C.

doi:10.1007/s00330-018-5807-2

Cited by 12 publications

(7 citation statements)

References 18 publications

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“…Hand tenosynovitis in patients with early arthritis or early RA was significantly associated with morning stiffness, impairment of hand function, and hand dexterity [26–29]. Given the advanced performance of MRI on visualizing tendons and inflammation, all kinds of quantification or scoring systems for MRI tenosynovitis in hands were shaped constantly [14, 30–34]. The 2016 updated RAMRIS first launched a standardized score system for MRI tenosynovitis in hands and recommended tenosynovitis as well as synovitis, osteitis, and bone erosion are crucial MRI features in management of RA [11].…”

Section: Discussionmentioning

confidence: 99%

The value of MRI examination on bilateral hands including proximal interphalangeal joints for disease assessment in patients with early rheumatoid arthritis: a cross-sectional cohort study

Yang

Jun-wei

et al. 2019

Arthritis Res Ther

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BackgroundBilateral hands including proximal interphalangeal joints (PIPJs) are recommended on physical, X-ray radiographic, or ultrasonographic examination by clinical guidelines of rheumatoid arthritis (RA), but MRI still tends to examine unilateral wrists and/or MCPJs. We aimed to demonstrate the advantages of MRI examination on bilateral hands including PIPJs for disease assessment in early RA patients.MethodsActive early RA patients received 3.0T whole-body MRI examination with contrast-enhanced imaging on bilateral wrists, MCPJs, and PIPJs. MRI features were scored referring to the updated RAMRIS. Clinical assessments were conducted on the day of MRI examination.ResultsThe mean time of MRI examination was 24 ± 3 min. MRI bone erosion in MCPJs would be missed-diagnosed in 23% of patients if non-dominant MCPJs were scanned unilaterally, while osteitis in MCPJs would be missed-diagnosed in 16% of patients if dominant MCPJs were scanned unilaterally. MRI synovitis severity was also asymmetric: 21% of patients showing severe synovitis unilaterally in non-dominant MCPJs/PIPJs and other 20% showing severe synovitis unilaterally in dominant MCPJs/PIPJs. Among these early RA patients, MRI tenosynovitis occurred the most frequently in wrist extensor compartment I, while MRI examination on bilateral hands demonstrated no overuse influence present. However, overuse should be considered in dominant PIPJ2, PIPJ4, and IPJ of thumb of which MRI tenosynovitis prevalence was respectively 18%, 17%, or 16% higher than the non-dominant counterparts. Early MRI abnormality of nervus medianus secondary to severe tenosynovitis occurred either in dominant or non-dominant wrists; MRI of unilateral hands would take a risk of missed-diagnosis. Common MRI findings in PIPJs were synovitis and tenosynovitis, respectively in 87% and 69% of patients. MRI tenosynovitis prevalence in IPJ of thumb or PIPJ5 was much higher than the continued wrist flexor compartments. MRI synovitis or tenosynovitis in PIPJs independently increased more than twice probability of joint tenderness (OR = 2.09 or 2.83, both p < 0.001).ConclusionsIn consideration of asymmetric MRI features in early RA, potential overuse influence for certain tenosynovitis in dominant hands, and high prevalence of MRI findings in PIPJs, MRI examination on bilateral hands including PIPJs is deserved for disease assessment in early RA patients.

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Section: Discussionmentioning

confidence: 99%

The value of MRI examination on bilateral hands including proximal interphalangeal joints for disease assessment in patients with early rheumatoid arthritis: a cross-sectional cohort study

Yang

Jun-wei

et al. 2019

Arthritis Res Ther

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“…Similarly to synovitis, quantification of tenosynovitis requires an automatic segmentation of tendons, in order to define the surrounding synovium and quantify inflammation in contrast-enhanced MRI. Our group has developed such an automatic framework in order to quantify tenosynovitis in the wrist 2. Next steps in our research are to apply deep learning in order to detect tendons (see figure 3) and quantify tenosynovitis, but also to classify the entire image by a single CNN.…”

Section: Applications Of Ai In Imaging In Rheumatologymentioning

confidence: 99%

“…In the human retina, the output of the receptors are preprocessed by retinal ganglion cells in a similar way 1. Therefore, a CNN not only models learning mechanisms but also seems to mimic evolution of human vision into ‘hard-wired’ circuits, for example, in the retina (see figure 3, for an example of a CNN for detecting anatomical structures in the wrist) 2 3…”

Section: Introductionmentioning

confidence: 99%

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Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

Stoel

2020

RMD Open

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After decades of basic research with many setbacks, artificial intelligence (AI) has recently obtained significant breakthroughs, enabling computer programs to outperform human interpretation of medical images in very specific areas. After this shock wave that probably exceeds the impact of the first AI victory of defeating the world chess champion in 1997, some reflection may be appropriate on the consequences for clinical imaging in rheumatology. In this narrative review, a short explanation is given about the various AI techniques, including ‘deep learning’, and how these have been applied to rheumatological imaging, focussing on rheumatoid arthritis and systemic sclerosis as examples. By discussing the principle limitations of AI and deep learning, this review aims to give insight into possible future perspectives of AI applications in rheumatology.

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“…Cao et al [ 18 ] improved the edge detection performance by introducing the Otsu algorithm to optimize the double threshold of the Canny operator and increased the image processing speed by about 3.4 times. However, the wrist joint tissue structure is complex, and the artifacts and gray unevenness caused by MRI scanning are still difficult for automatic segmentation [ 19 ]. Based on the above reasons, an automatic segmentation method for wrist MRI images was proposed in this study based on the edge detection algorithm, which improved the diagnostic accuracy of WJI.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries

2021

Contrast Media & Molecular Imaging

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Background. Wrist joint injury refers to the injury of the wrist joint caused by excessive stretching of the ligaments and joint capsules around the joint caused by indirect violence. The tissue structure of the wrist joint is complex, and the clinical diagnosis effect is poor. Methods. The purpose of this study was to improve the diagnostic accuracy of wrist joint injuries and provide evidence for imaging analysis and automatic diagnosis of lesions in patients with wrist joint injuries. The Canny algorithm was adopted to extract the edge features of the patient’s magnetic resonance imaging (MRI) image, and the particle swarm optimization-support vector machine (PSO-SVM) algorithm was applied to segment the lesion. The image processing effect of the algorithm was evaluated by taking peak signal to noise ratio (PSNR), mean square error (MSE), figure of merit (FOM), and structural similarity (SSIM) as indicators. The accuracy, sensitivity, specificity, and Dice similarity coefficient of the algorithm were analyzed to evaluate the diagnostic accuracy in WJI. Results. Compared with the Gradient Vector Flo (GVF) algorithm and the Elastic Automatic Region Growing (ERG) algorithm, the edge stability of the PSO-SVM algorithm was stable above 0.9. After the quality of images processed using different algorithms was analyzed, it was found that the PSNR of the PSO-SVM algorithm was 26.891 ± 5.331 dB, the MSE was 0.0014 ± 0.0003, the FOM was 0.8832 ± 0.0957, and the SSIM was 0.9032 ± 0.0807. The four indicators were all much better than those of the GVF algorithm and the EARG algorithm, showing statistically obvious differences ( P < 0.05). Analysis on diagnostic accuracy of different algorithms for WJI suggested that the diagnostic accuracy of the PSO-SVM algorithm was 0.9413, the sensitivity was 0.9129, the specificity was 0.9088, and the Dice similarity coefficient was 0.8715. The four indicators all showed statistically great difference compared with those of the GVF algorithm and the EARG algorithm ( P < 0.05). Conclusions. The PSO-SVM algorithm showed excellent edge detection performance and higher accuracy in the diagnosis of WJI, which can assist clinicians in the clinical auxiliary diagnosis of WJI.

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Automatic quantification of tenosynovitis on MRI of the wrist in patients with early arthritis: a feasibility study

Cited by 12 publications

References 18 publications

The value of MRI examination on bilateral hands including proximal interphalangeal joints for disease assessment in patients with early rheumatoid arthritis: a cross-sectional cohort study

The value of MRI examination on bilateral hands including proximal interphalangeal joints for disease assessment in patients with early rheumatoid arthritis: a cross-sectional cohort study

Use of artificial intelligence in imaging in rheumatology – current status and future perspectives

Magnetic Resonance Imaging Image Segmentation under Edge Detection Intelligent Algorithm in Diagnosis of Surgical Wrist Joint Injuries

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