Extremity magnetic resonance imaging in rheumatoid arthritis: Updated literature review

Rapid screening for autoimmune diseases using Fourier transform infrared spectroscopy and deep learning algorithms

Introduce

Ankylosing spondylitis (AS), rheumatoid arthritis (RA), and osteoarthritis (OA) are three rheumatic immune diseases with many common characteristics. If left untreated, they can lead to joint destruction and functional limitation, and in severe cases, they can cause lifelong disability and even death. Studies have shown that early diagnosis and treatment are key to improving patient outcomes. Therefore, a rapid and accurate method for rapid diagnosis of diseases has been established, which is of great clinical significance for realizing early diagnosis of diseases and improving patient prognosis.

Methods

This study was based on Fourier transform infrared spectroscopy (FTIR) combined with a deep learning model to achieve non-invasive, rapid, and accurate differentiation of AS, RA, OA, and healthy control group. In the experiment, 320 serum samples were collected, 80 in each group. AlexNet, ResNet, MSCNN, and MSResNet diagnostic models were established by using a machine learning algorithm.

Result

The range of spectral wave number measured by four sets of Fourier transform infrared spectroscopy is 700-4000 cm-1. Serum spectral characteristic peaks were mainly at 1641 cm-1(amide I), 1542 cm-1(amide II), 3280 cm-1(amide A), 1420 cm-1(proline and tryptophan), 1245 cm-1(amide III), 1078 cm-1(carbohydrate region). And 2940 cm-1 (mainly fatty acids and cholesterol). At the same time, AlexNet, ResNet, MSCNN, and MSResNet diagnostic models are established by using machine learning algorithms. The multi-scale MSResNet classification model combined with residual blocks can use convolution modules of different scales to extract different scale features and use resblocks to solve the problem of network degradation, reduce the interference of spectral measurement noise, and enhance the generalization ability of the network model. By comparing the experimental results of the other three models AlexNet, ResNet, and MSCNN, it is found that the MSResNet model has the best diagnostic performance and the accuracy rate is 0.87.

Conclusion

The results prove the feasibility of serum Fourier transform infrared spectroscopy combined with a deep learning algorithm to distinguish AS, RA, OA, and healthy control group, which can be used as an effective auxiliary diagnostic method for these rheumatic immune diseases.

Biomarkers (mRNAs and non-coding RNAs) for the diagnosis and prognosis of rheumatoid arthritis

In recent years, diagnostic and therapeutic approaches for rheumatoid arthritis (RA) have continued to improve. However, in the advanced stages of the disease, patients are unable to achieve long-term clinical remission and often suffer from systemic multi-organ damage and severe complications. Patients with RA usually have no overt clinical manifestations in the early stages, and by the time a definitive diagnosis is made, the disease is already at an advanced stage. RA is diagnosed clinically and with laboratory tests, including the blood markers C-reactive protein (CRP) and erythrocyte sedimentation rate (ESR) and the autoantibodies rheumatoid factor (RF) and anticitrullinated protein antibodies (ACPA). However, the presence of RF and ACPA autoantibodies is associated with aggravated disease, joint damage, and increased mortality, and these autoantibodies have low specificity and sensitivity. The etiology of RA is unknown, with the pathogenesis involving multiple factors and clinical heterogeneity. The early diagnosis, subtype classification, and prognosis of RA remain challenging, and studies to develop minimally invasive or non-invasive biomarkers in the form of biofluid biopsies are becoming more common. Non-coding RNA (ncRNA) molecules are composed of long non-coding RNAs, small nucleolar RNAs, microRNAs, and circular RNAs, which play an essential role in disease onset and progression and can be used in the early diagnosis and prognosis of RA. In this review of the diagnostic and prognostic approaches to RA disease, we provide an overview of the current knowledge on the subject, focusing on recent advances in mRNA-ncRNA as diagnostic and prognostic biomarkers from the biofluid to the tissue level.

Contemporary imaging of rheumatoid arthritis: Clinical role of ultrasound and MRI

Magnetic resonance imaging (MRI) and musculoskeletal ultrasound (MSUS) are sensitive imaging modalities used by clinicians to assist in decision-making in the management of rheumatoid arthritis (RA). This review will examine the utility of MRI and MSUS in diagnosing RA, predicting RA flares, tapering therapy, assessing remission, and examining difficult periarticular features. We will also outline the strengths and weaknesses of utilizing MRI and MSUS as outcome measures in the management of RA.

Update on the Pathomechanism, Diagnosis, and Treatment Options for Rheumatoid Arthritis

Rheumatoid arthritis (RA) is an autoimmune disease that involves multiple joints bilaterally. It is characterized by an inflammation of the tendon (tenosynovitis) resulting in both cartilage destruction and bone erosion. While until the 1990s RA frequently resulted in disability, inability to work, and increased mortality, newer treatment options have made RA a manageable disease. Here, great progress has been made in the development of disease-modifying anti-rheumatic drugs (DMARDs) which target inflammation and thereby prevent further joint damage. The available DMARDs are subdivided into (1) conventional synthetic DMARDs (methotrexate, hydrochloroquine, and sulfadiazine), (2) targeted synthetic DMARDs (pan-JAK- and JAK1/2-inhibitors), and (3) biologic DMARDs (tumor necrosis factor (TNF)-α inhibitors, TNF-receptor (R) inhibitors, IL-6 inhibitors, IL-6R inhibitors, B cell depleting antibodies, and inhibitors of co-stimulatory molecules). While DMARDs have repeatedly demonstrated the potential to greatly improve disease symptoms and prevent disease progression in RA patients, they are associated with considerable side-effects and high financial costs. This review summarizes our current understanding of the underlying pathomechanism, diagnosis of RA, as well as the mode of action, clinical benefits, and side-effects of the currently available DMARDs.

The role of imaging in rheumatoid arthritis

Conventional radiographs of the hands and feet have traditionally been used in the diagnosis, management and monitoring of patients with rheumatoid arthritis (RA). However, they are not sensitive enough to detect changes early in the disease process. Erosions may only be visible up to two years after the onset of disease, and soft tissue involvement may not be detected at all. Early diagnosis can also be made challenging as markers such as erythrocyte sedimentation rate and C-reactive protein may be normal in up to 20% – 25% of cases. The latest classification criteria (American College of Rheumatology/European League Against Rheumatism [ACR/EULAR] Rheumatoid Arthritis Classification criteria 2010), often used to diagnose RA, incorporate the role of ultrasound and magnetic resonance imaging detection of synovitis, enabling earlier diagnosis and correct classification of patients. This article looks at the role of the various imaging modalities used in the diagnosis and management of RA.

Magnetic resonance imaging in individuals at risk of rheumatoid arthritis

Individuals with rheumatoid arthritis (RA) benefit from early diagnosis and initiation of therapy. There can be delays in both due to diagnostic uncertainties. Imaging modalities, including magnetic resonance imaging (MRI), can detect inflammation earlier than clinical examination alone in early RA patients. Furthermore, the predictive role of MRI for the future development of RA has recently been explored in 'at-risk' individuals. This review details the use of MRI in early and undifferentiated arthritis and summarises the studies to date in individuals at risk of RA.

Oval gradient coils for an open magnetic resonance imaging system with a vertical magnetic field

Existing open magnetic resonance imaging (MRI) systems use biplanar gradient coils for the spatial encoding of signals. We propose using novel oval gradient coils for an open vertical-field MRI. We designed oval gradients for a 0.3T open MRI system and showed that such a system could outperform a traditional biplanar gradient system while maintaining adequate gradient homogeneity and subject accessibility. Such oval gradient coils would exhibit high efficiency, low inductance and resistance, and high switching capability. Although the designed oval Y and Z coils showed more heat dissipation and less cooling capability than biplanar coils with the same gap, they showed an efficient heat-dissipation path to the surrounding air, which would alleviate the heat problem. The performance of the designed oval-coil system was demonstrated experimentally by imaging a human hand.