Applications of artificial intelligence in musculoskeletal ultrasound: narrative review.

Ultrasonography (US) has become a valuable imaging tool for the examination of the musculoskeletal system. It provides important diagnostic information and it can also be very useful in the assessment of disease activity and treatment response. US has gained widespread use in rheumatology practice because it provides real time and dynamic assessment, although it is dependent on the examiner's experience. The implementation of artificial intelligence (AI) techniques in the process of image recognition and interpretation has the potential to overcome certain limitations related to physician-dependent assessment, such as the variability in image acquisition. Multiple studies in the field of AI have explored how integrated machine learning algorithms could automate specific tissue recognition, diagnosis of joint and muscle pathology, and even grading of synovitis which is essential for monitoring disease activity. AI-based techniques applied in musculoskeletal US imaging focus on automated segmentation, image enhancement, detection and classification. AI-based US imaging can thus improve accuracy, time efficiency and offer a framework for standardization between different examinations. This paper will offer an overview of current research in the field of AI-based ultrasonography of the musculoskeletal system with focus on the applications of machine learning techniques in the examination of joints, muscles and peripheral nerves, which could potentially improve the performance of everyday clinical practice.

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

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.