Brain predictors of fatigue in rheumatoid arthritis: A machine learning study

Machine learning application in autoimmune diseases: State of art and future prospectives

Autoimmune diseases are a group of disorders resulting from an alteration of immune tolerance, characterized by the formation of autoantibodies and the consequent development of heterogeneous clinical manifestations. Diagnosing autoimmune diseases is often complicated, and the available prognostic tools are limited. Machine learning allows us to analyze large amounts of data and carry out complex calculations quickly and with minimal effort. In this work, we examine the literature focusing on the use of machine learning in the field of the main systemic (systemic lupus erythematosus and rheumatoid arthritis) and organ-specific autoimmune diseases (type 1 diabetes mellitus, autoimmune thyroid, gastrointestinal, and skin diseases). From our analysis, interesting applications of machine learning emerged for developing algorithms useful in the early diagnosis of disease or prognostic models (risk of complications, therapeutic response). Subsequent studies and the creation of increasingly rich databases to be supplied to the algorithms will eventually guide the clinician in the diagnosis, allowing intervention when the pathology is still in an early stage and immediately directing towards a correct therapeutic approach.

Fibromyalgia and centralized pain in the rheumatoid arthritis patient

PURPOSE OF REVIEW

Individuals with rheumatoid arthritis (RA) have traditionally been characterized as having nociceptive pain, leading to the assumption that effective immunosuppression should be enough to provide effective pain management. However, despite therapeutic advancements providing excellent control of inflammation, patients continue to have significant pain and fatigue. The presence of concurrent fibromyalgia, driven by augmented central nervous system processing and largely unresponsive to peripheral therapies, may contribute to this pain persistence. This review provides updates on fibromyalgia and RA as relevant for the clinician.

RECENT FINDINGS

Patients with RA have high levels of concomitant fibromyalgia and nociplastic pain. The presence of fibromyalgia can lead to higher scores on disease measures, erroneously indicating that worse disease is presently leading to the increased use of immunosuppressives and opioids. Disease scores that provide a comparison between patient-reported and provider-reported and clinical factors may be helpful to indicate centralized pain. IL-6 and Janus kinase inhibitors, in addition to targeting peripheral inflammation, may provide pain relief by acting on peripheral and central pain pathways.

SUMMARY

Central pain mechanisms that may be contributing to pain in RA are common and should be distinguished from pain directly arising from peripheral inflammation.

Brain mapping inflammatory-arthritis-related fatigue in the pursuit of novel therapeutics

Despite developments in pharmacological treatments, chronic fatigue is an unresolved issue for most people with inflammatory arthritis that severely disrupts their personal and working lives. Fatigue in these patients is not strongly linked with peripheral disease activity but is associated with CNS-derived symptoms such as chronic pain, sleep disturbance, and depression. Therefore, a neurobiological basis should be considered when pursuing novel fatigue-specific therapeutics. In this Review, we focus on clinical imaging biomarkers that map candidate brain regions and are crucial in fatigue pathophysiology. We then evaluate neuromodulation techniques that could affect these candidate brain regions and are potential treatment strategies for fatigue in patients with inflammatory arthritis. We delineate work that is still required for neuroimaging and neuromodulation to eventually become part of a clinical pathway to treat and manage fatigue.

Artificial Intelligence Driven Biomedical Image Classification for Robust Rheumatoid Arthritis Classification

Recently, artificial intelligence (AI) including machine learning (ML) and deep learning (DL) models has been commonly employed for the automated disease diagnosis process. AI in biological and biomedical imaging is an emerging area and will be a future trend in the field. At the same time, biomedical images can be used for the classification of Rheumatoid arthritis (RA) diseases. RA is an autoimmune illness that affects the musculoskeletal system causing systemic, inflammatory and chronic effects. The disease frequently becomes progressive and decreases physical function, causing articular damage, suffering, and fatigue. After a time, RA causes harm to the cartilage of the joints and bones, weakens the tendons and joints, and finally causes joint destruction. Sensors (thermal infrared camera sensor, accelerometers and wearable sensors) are more commonly employed to collect data for RA. This study develops an Automated Rheumatoid Arthritis Classification using an Arithmetic Optimization Algorithm with Deep Learning (ARAC-AOADL) model. The goal of the presented ARAC-AOADL technique lies in the classification of health disorders depending upon RA and orthopaedics. Primarily, the presented ARAC-AOADL technique pre-processes the input images by median filtering (MF) technique. Then, the ARAC-AOADL technique uses AOA with an enhanced capsule network (ECN) model to produce feature vectors. For RA classification, the ARAC-AOADL technique uses a multi-kernel extreme learning machine (MKELM) model. The experimental result analysis of the ARAC-AOADL technique on a benchmark dataset reported a maximum accuracy of 98.57%. Therefore, the ARAC-AOADL technique can be employed for accurate and timely RA classification.