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

Artificial intelligence in musculoskeletal ultrasound imaging

Ultrasonography (US) is noninvasive and offers real-time, low-cost, and portable imaging that facilitates the rapid and dynamic assessment of musculoskeletal components. Significant technological improvements have contributed to the increasing adoption of US for musculoskeletal assessments, as artificial intelligence (AI)-based computer-aided detection and computer-aided diagnosis are being utilized to improve the quality, efficiency, and cost of US imaging. This review provides an overview of classical machine learning techniques and modern deep learning approaches for musculoskeletal US, with a focus on the key categories of detection and diagnosis of musculoskeletal disorders, predictive analysis with classification and regression, and automated image segmentation. Moreover, we outline challenges and a range of opportunities for AI in musculoskeletal US practice.11Nsciescopu

Assessment of inflammation in patients with rheumatoid arthritis using thermography and machine learning: a fast and automated technique

Objectives Sensitive detection of joint inflammation in rheumatoid arthritis (RA) is crucial to the success of the treat-to-target strategy. In this study, we characterise a novel machine learning-based computational method to automatically assess joint inflammation in RA using thermography of the hands, a fast and non-invasive imaging technique. Methods We recruited 595 patients with arthritis and osteoarthritis, as well as healthy subjects at two hospitals over 4 years. Machine learning was used to assess joint inflammation from the thermal images of the hands using ultrasound as the reference standard, obtaining a Thermographic Joint Inflammation Score (ThermoJIS). The machine learning model was trained and tuned using data from 449 participants with different types of arthritis, osteoarthritis or without rheumatic disease (development set). The performance of the method was evaluated based on 146 patients with RA (validation set) using Spearman's rank correlation coefficient, area under the receiver-operating curve (AUROC), average precision, sensitivity, specificity, positive and negative predictive value and F1-score. Results ThermoJIS correlated moderately with ultrasound scores (grey-scale synovial hypertrophy=0.49, p<0.001; and power Doppler=0.51, p<0.001). The AUROC for ThermoJIS for detecting active synovitis was 0.78 (95% CI, 0.71 to 0.86; p<0.001). In patients with RA in clinical remission, ThermoJIS values were significantly higher when active synovitis was detected by ultrasound. Conclusions ThermoJIS was able to detect joint inflammation in patients with RA, even in those in clinical remission. These results open an opportunity to develop new tools for routine detection of joint inflammation

Ultrasound and fluorescence optical imaging biomarkers for early diagnosis and prediction of rheumatoid arthritis

Prevention of rheumatoid arthritis (RA) is most desirable together with curative treatment which is, however, not yet available. Today, the correct timely diagnosis and early treatment interventions to prevent disease progression remain the best options for our patients. In this thesis, I explore the diagnostic and predictive value of musculoskeletal ultrasound (MSUS) and fluorescence optical imaging (FOI) in identifying biological features on images indicative of existing or emerging joint inflammation (synovitis). In study 1, we tested and compared the diagnostic utility of FOI with clinical examination and musculoskeletal ultrasound (MSUS) to detect active synovitis in 872 joints of 26 patients with different rheumatic diseases (46% early RA). Fluorescence optical imaging proved to be 80% sensitive and 96% specific, having a 77% positive predictive value (PPV) and 97% negative predictive value (NPV) for detecting silent synovitis. In study 2 we showed FOI’s ability to quantify digital disease activity (DACT) scores of 1326 joints in 39 early RA patients to be 81% sensitive and 90% specific, with 96% PPV and 61% NPV. These results justify FOI use in clinical practice, to assist the rheumatologist to make an earlier diagnosis with greater confidence. Unsupervised cluster differences emerged for seropositive and seronegative RA patients showing FOI’s ability to objectively quantify hand joint inflammation using novel DACT scoring methods. In study 3 we report good association among the two ultrasound semi-quantitative scoring (SQS) methods to that of a novel quantitative scoring (QS) measure of color Doppler pixel counts in 37 established RA patients. Although SQS well correlated with QS to assess active synovitis, the SQS methods lacked visual perceptions of raters to distinguish between grade cut-offs which may help to further revise the criteria used to objectively quantify disease activity. In study 4, we show the value of ultrasound and immune-inflammatory biomarkers in predicting arthritis onset in individuals positive for Anti-CCP with musculoskeletal complaints at risk of RA development. We propose the recognition of a high-risk RA phase characterized by presence of certain ACPA reactivities, IL15-Rα, IL6; and ultrasound detected tenosynovitis, and possibilities to identify (low and high) risk groups for arthritis progression. Overall, our findings on imaging contribute towards a) silent synovitis detection despite negative clinical investigation, b) objective quantitative measures to monitor the effects of RA therapy and c) early identification of certain predictive imaging and biological features/biomarkers that precede arthritis development (tenosynovitis and/or bursitis) in individuals at risk for developing RA, enabling closer monitoring and early diagnosis

Quantitative imaging by pixel-based contrast-enhanced ultrasound reveals a linear relationship between synovial vascular perfusion and the recruitment of pathogenic IL-17A-F+IL-23+ CD161+ CD4+ T helper cells in psoriatic arthritis joints

To develop quantitative imaging biomarkers of synovial tissue perfusion by pixel-based contrast-enhanced ultrasound (CEUS), we studied the relationship between CEUS synovial vascular perfusion and the frequencies of pathogenic T helper (Th)-17 cells in psoriatic arthritis (PsA) joints. Eight consecutive patients with PsA were enrolled in this study. Gray scale CEUS evaluation was performed on the same joint immediately after joint aspiration, by automatic assessment perfusion data, using a new quantification approach of pixel-based analysis and the gamma-variate model. The set of perfusional parameters considered by the time intensity curve includes the maximum value (peak) of the signal intensity curve, the blood volume index or area under the curve, (BVI, AUC) and the contrast mean transit time (MTT). The direct ex vivo analysis of the frequencies of SF IL17A-F+CD161+IL23+ CD4+ T cells subsets were quantified by fluorescence-activated cell sorter (FACS). In cross-sectional analyses, when tested for multiple comparison setting, a false discovery rate at 10%, a common pattern of correlations between CEUS Peak, AUC (BVI) and MTT parameters with the IL17A-F+IL23+ - IL17A-F+CD161+ - and IL17A-F+CD161+IL23+ CD4+ T cells subsets, as well as lack of correlation between both peak and AUC values and both CD4+T and CD4+IL23+ T cells, was observed. The pixel-based CEUS assessment is a truly measure synovial inflammation, as a useful tool to develop quantitative imaging biomarker for monitoring target therapeutics in PsA. © 2016, International League of Associations for Rheumatology (ILAR)

Vascular Supply of the Metacarpophalangeal Joint

OBJECTIVE: To describe in detail the arterial vasculature of metacarpophalangeal joints 2–5 on cadaver specimens and to compare it to ultrasound imaging of healthy subjects. METHODS: Eighteen hands of donated human cadavers were arterially injected and investigated with either corrosion casting or cryosectioning. Each layer of cryosectioned specimens was photographed in high-resolution. Images were then segmented for arterial vessels of the metacarpophalangeal (MCP) joints 2–5. The arterial pattern of the joints was reconstructed from the segmented images and from the corrosion cast specimens. Both hands of ten adult healthy volunteers were scanned focusing on the vasculature of the same joints with high-end ultrasound imaging, including color Doppler. Measurements were made on both cryosectioned arteries and Doppler images. RESULTS: The arterial supply of MCP joints 2–5 divides into a metacarpal and a phalangeal territory, respectively. The metacarpal half receives arteries from the palmar metacarpal arteries or proper palmar digital arteries, while the phalangeal half is supplied by both proper and common palmar digital arteries. Comparing anatomical and ultrasonographic results, we determined the exact anatomic location of normal vessels using Doppler images acquired of healthy joints. All, except three branches, were found with less than 50% frequency using ultrasound. Doppler signals were identified significantly more frequently in MCP joints 2–3 than on 4–5 (p < 0.0001). Similarly, Doppler signals differed in the number of detectable small, intraarticular vessels (p < 0.009), but not that of the large extraarticular ones (p < 0.1373). When comparing measurements acquired by ultrasound and on cadaver vessels, measurements using the former technique were found to be larger in all joints (p < 0.0001). CONCLUSION: Using morphological and ultrasonographic techniques, our study provides a high-resolution anatomical maps and an essential reference data set on the entire arterial vasculature of healthy human MCP 2–5 joints. We found that Doppler signal could be detected in less than 50% of the vessels of healthy volunteers except three locations. Intraarticular branches were detected with ultrasound imaging significantly more frequently on healthy MCP 2–3 joints, which should be taken into account when inflammatory and normal Doppler signals are evaluated. Our study also provides reference data for future, higher-resolution imaging techniques

Ultrasound imaging in joint and soft tissue inflammation

The use of ultrasound as an extended and more objective investigation performed as an extension of physical examination has a potential role in studying inflammation in different rheumatic diseases such as rheumatoid arthritis (RT) and spondylarthropathy (SpA). Rheumatoid arthritis is a chronic disease causing joint inflammation and destruction. Metacarpophalangeal (MCP) joint involvement is one of the earliest and most permanent signs of RA. US has been used to detect synovitis and erosions in MCP joints with high accuracy when compared to X-ray and magnetic resonance imaging (MRI). In RA joints, power Doppler has been used to detect increased blood flow as a potential sign of inflammation but grey-scale and power Doppler ultrasonography was not compared to another method to detect increased blood flow in MCP joints. After RA the next most common inflammatory group of diseases are the seronegative spondylarthropathies. In SpA joint inflammation and ankylosis occur in addition to periarticular enthesitis, which is one of the major hallmarks of the disease and has been poorly studied by ultrasonography. In order to reduce observer variation in musculoskeletal ultrasound examination to the level of other imaging methods it is necessary to avoid direct contact between the observer and the subject. This problem has been addressed in the aerospace industry and led to the development of air-coupled non-destructive testing. Air-coupled ultrasonography has the potential in medial imaging to exclude observer variation if it is able to depict human anatomy. There are currently no data regarding airborne ultrasound in the musculoskeletal ultrasound literature

ULTRASOUND IMAGING OF SYNOVITIS: RELATIONSHIP TO PATHOBIOLOGY AND RESPONSE TO THERAPY

PhdUltrasound (US) imaging has made significant progress over the past 20 years in relation to its role in inflammatory arthritis, and in particular, Rheumatoid Arthritis. Modern US machines provide crisp, detailed images of superficial anatomical structures which has facilitated the uptake of US imaging as an important assessment tool within the Rheumatology department. Diagnostic and prognostic information can now assist clinicians decisions with the goal of improving patient treatment and subsequent outcome. In addition, 3D US imaging has recently been suggested as an additional imaging modality with potential benefits in the assessment of in?ammatory arthritis. Recent work has focused on providing a reliable, responsive US joint count which can be assimilated into routine care as well as providing a platform for clinical research. Thus, my first aim was to show that a defined limited US data set, including 2D and 3D imaging, shows acceptable reliability. I demonstrate that both imaging modalities are reliable in terms of reading and image acquisition when restricted to a limited US data set. My second aim, was to demonstrate that a limited US data set is responsive. Using both a physiological and pharmacological trigger, I demonstrate that both 2D and 3D imaging are responsive and that combining US endpoints with DAS28 (Disease Activity Score - 28) increased the effect size and identifies treatment effects early. Despite notable advances in musculoskeletal US research, there is still need for better understanding of the pathophysiological correlates of ultrasound imaging. Therefore my final aim was to examine the relationship of Power Doppler Signal (PDS) and gray-scale synovial thickening with histological features of synovitis at a single joint level and with an extended joint US data set. I Firstly show that the harvesting of synovial tissue, using a minimally invasive US-guided biopsy technique, is safe and well tolerated by patients and that the quality of tissue and RNA extracted is good. Using this tissue collection method, I demonstrate a good correlation of US and histological parameters of synovitis (specifically CD68+ sub-lining macrophages) at a single joint level, in both an early and established RA cohort. This relationship is maintained if the US assessment is extended to a discrete US joint data set. Furthermore, within the knee joint I demonstrated that PDS correlates well with synovial tissue expression of inflammatory mediators of neoangiogenesis and histological assessment of synovial vascular area

Diagnostic imaging techniques and predictive factors in spondyloarthritis and psoriatic arthritis

Tesis doctoral inédita leída en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina. Fecha de lectura: 12-06-201