Objectives To comparatively assess the sonographic spectrum of carpal tunnel syndrome (CTS) in patients with rheumatoid arthritis (RA) and in patients with idiopathic CTS. Methods Fifty-seven RA patients and 25 idiopathic CTS patients were consecutively enrolled. The diagnosis of CTS in RA patients was made according to clinical history and examination. The following sonographic findings were assessed at carpal tunnel level: median nerve cross-sectional area (CSA) at the carpal tunnel proximal inlet, finger flexor tendons tenosynovitis, radio-carpal synovitis and intraneural power Doppler (PD) signal. Results CTS was diagnosed in 15/57 RA patients (26.3%). Twenty-three RA wrists with CTS, 84 RA wrists without CTS and 34 idiopathic CTS wrists were evaluated. The average CSA of the median nerve was higher in idiopathic CTS than in RA wrists with CTS (17.7 mm 2 vs 10.6 mm 2 , p < 0.01). A higher rate of inflammation of synovial structures (flexor tendons sheath and/or radio-carpal joint) was found in RA wrists with CTS compared with those without CTS (p = 0.04) and idiopathic CTS (p = 0.02). Intraneural PD signal was more common in CTS (in both RA and idiopathic CTS) wrists compared with wrists without CTS (p < 0.01). Conclusion The sonographic spectrum of CTS in RA patients is characterized by an inflammatory pattern, defined by the presence of finger flexor tendons tenosynovitis and/or radio-carpal joint synovitis. Conversely, a marked median nerve swelling is the dominant feature in idiopathic CTS. Intraneural PD signal is a frequent finding in both conditions. Keywords Carpal tunnel syndrome. Nerve compression syndromes. Rheumatoid arthritis. Ultrasonography Key Points • Carpal tunnel syndrome (CTS) associated with rheumatoid arthritis (RA) and idiopathic CTS has distinct ultrasound patterns. • The most characteristic sonographic features of CTS in RA patients are those indicative of synovial tissue inflammation at carpal tunnel level. Conversely, marked median nerve swelling is the dominant finding in idiopathic CTS. • Intraneural power Doppler signal is a frequent finding in both conditions. • In patients with CTS, differently from electrophysiology, US can provide clues prompting a rheumatology referral in case of prominent inflammatory findings at carpal tunnel level.

Objectives 1) To explore the ultrasound (US) findings of muscle mass, muscle quality and muscle stiffness in systemic lupus erythematosus (SLE) patients and healthy subjects; 2) To investigate the relationship between the US muscle findings and physical performance in SLE patients and healthy subjects. Methods Quadriceps muscle thickness for muscle mass, muscle echogenicity (using a visual semiquantitative scale and grayscale analysis with histograms) for muscle quality, and shear-wave elastography (SWE) for muscle stiffness, were assessed in 30 SLE patients (without previous/current myositis or neuromuscular disorders) and 15 age, sex, and BMI-matched healthy subjects. Hand grip strength test and short physical performance battery (SPPB) were carried out in the same populations. Results No difference in the quadriceps muscle thickness was observed between SLE patients and healthy subjects (35.2 mm ±SD 6.8 vs 34.8 mm ±SD 6.0 respectively, p = 0.79). Conversely, muscle echogenicity was significantly increased in SLE patients (visual semiquantitative scale: 1.7 ±SD 1.0 vs 0.3 ±SD 0.5, respectively, p &lt; 0.01; grayscale analysis with histograms: 87.4 mean pixels ±SD 18.8 vs 70.1 mean pixels ±SD 14.0 respectively, p &lt; 0.01). Similarly, SWE was significantly lower in SLE patients compared with healthy subjects [1.5 m/s (IQR 0.3) vs 1.6 m/s (IQR 0.2) respectively, p = 0.01). Muscle echogenicity was inversely correlated with grip strength (visual semiquantitative scale Rho:-0.47, p = 0.01; grayscale analysis with histograms Rho:-0.41, p &lt; 0.01) and SPPB (visual semiquantitative scale Rho:-0.50, p &lt; 0.01; grayscale analysis with histograms Rho:-0,46, p &lt; 0.01). Conclusions US assessment of muscle echogenicity and stiffness is useful for the early detection of muscle involvement in SLE patients.

Purpose of Review To highlight the potential uses and applications of imaging in the assessment of the most common and relevant musculoskeletal (MSK) manifestations in systemic lupus erythematosus (SLE). Recent Findings Ultrasound (US) and magnetic resonance imaging (MRI) are accurate and sensitive in the assessment of inflammation and structural damage at the joint and soft tissue structures in patients with SLE. The US is particularly helpful for the detection of joint and/or tendon inflammation in patients with arthralgia but without clinical synovitis, and for the early identification of bone erosions. MRI plays a key role in the early diagnosis of osteonecrosis and in the assessment of muscle involvement (i.e., myositis and myopathy). Conventional radiography (CR) remains the traditional gold standard for the evaluation of structural damage in patients with joint involvement, and for the study of bone pathology. The diagnostic value of CR is affected by the poor sensitivity in demonstrating early structural changes at joint and soft tissue level. Computed tomography allows a detailed evaluation of bone damage. However, the inability to distinguish different soft tissues and the need for ionizing radiation limit its use to selected clinical circumstances. Nuclear imaging techniques are valuable resources in patients with suspected bone infection (i.e., osteomyelitis), especially when MRI is contraindicated. Finally, dual energy X-ray absorptiometry represents the imaging mainstay for the assessment and monitoring of bone status in patients with or at-risk of osteoporosis. Summary Imaging provides relevant and valuable information in the assessment of MSK involvement in SLE.

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Background Deep learning applied to ultrasound (US) can provide a feedback to the sonographer about the correct identification of scanned tissues and allows for faster and standardized measurements. The most frequently adopted parameter for US diagnosis of carpal tunnel syndrome is the increasing of the cross-sectional area (CSA) of the median nerve. Our aim was to develop a deep learning algorithm, relying on convolutional neural networks (CNNs), for the localization and segmentation of the median nerve and the automatic measurement of its CSA on US images acquired at the proximal inlet of the carpal tunnel. Methods Consecutive patients with rheumatic and musculoskeletal disorders were recruited. Transverse US images were acquired at the carpal tunnel inlet, and the CSA was manually measured. Anatomical variants were registered. The dataset consisted of 246 images (157 for training, 40 for validation, and 49 for testing) from 103 patients each associated with manual annotations of the nerve boundary. A Mask R-CNN, state-of-the-art CNN for image semantic segmentation, was trained on this dataset to accurately localize and segment the median nerve section. To evaluate the performances on the testing set, precision (Prec), recall (Rec), mean average precision (mAP), and Dice similarity coefficient (DSC) were computed. A sub-analysis excluding anatomical variants was performed. The CSA was automatically measured by the algorithm. Results The algorithm correctly identified the median nerve in 41/49 images (83.7%) and in 41/43 images (95.3%) excluding anatomical variants. The following metrics were obtained (with and without anatomical variants, respectively): Prec 0.86 ± 0.33 and 0.96 ± 0.18, Rec 0.88 ± 0.33 and 0.98 ± 0.15, mAP 0.88 ± 0.33 and 0.98 ± 0.15, and DSC 0.86 ± 0.19 and 0.88 ± 0.19. The agreement between the algorithm and the sonographer CSA measurements was excellent [ICC 0.97 (0.94–0.98)]. Conclusions The developed algorithm has shown excellent performances, especially if excluding anatomical variants. Future research should aim at expanding the US image dataset including a wider spectrum of normal anatomy and pathology. This deep learning approach has shown very high potentiality for a fully automatic support for US assessment of carpal tunnel syndrome.