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ObjectiveTo explore the association of the Outcome Measures in Rheumatology ultrasound (US) entheseal abnormalities with the presence of US joint bone erosions in psoriatic arthritis (PsA).MethodsConsecutive patients with PsA were included in this cross-sectional study. Demographic and clinical variables were collected. A bilateral US assessment was carried out at the following entheses: plantar fascia, and the quadriceps, patellar (proximal and distal), and Achilles tendons. The following US entheseal abnormalities were registered: hypoechogenicity, thickening, Doppler signal < 2 mm from the bony cortex, calcification/enthesophyte, and bone erosion. The presence of US joint bone erosions was investigated at the second and fifth metacarpophalangeal joints, ulnar head, and fifth metatarsophalangeal (MTP) joint, bilaterally, as well as at the level of the most inflamed joint on physical examination. Multiple linear regression analysis was performed to identify clinical and/or US variables associated with US-detected joint bone erosions.ResultsA total of 104 patients with PsA were enrolled. At least 1 joint bone erosion was found in 47 of 104 patients (45.2%). Bone erosions were most frequently detected at the fifth MTP joint level (42/208 joints [20.2 %] in 32/104 patients [30.8%]). In the multivariate model, only a power Doppler (PD) signal at the enthesis (P< 0.001, standardized β = 0.51), bone erosions at the enthesis (P= 0.02, standardized β = 0.20), PsA disease duration (P= 0.04, standardized β = 0.17), and greyscale joint synovitis (P= 0.03, standardized β = 0.42) were associated with US-detected joint bone erosions.ConclusionPD signal and bone erosions at the enthesis represent sonographic biomarkers of a more severe subset of PsA in terms of US-detected joint erosive damage.

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.