Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview

Gutiérrez-Martínez, Josefina; Pineda, Carlos; Sandoval, Hugo; Bernal-González, Araceli

doi:10.1007/s10067-019-04791-z

Cited by 11 publications

(9 citation statements)

References 57 publications

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“…The imaging technologies typically used to survey cartilage degeneration during rheumatic diseases are magnetic resonance imaging (MRI) and ultrasonography (US) . In order to make the interpretation of these data accessible to rheumatologists without extensive expertise in clinical imaging, standardization protocols of image acquisition have been proposed to account for anatomic diversity and automatized analysis tools based on deep‐convolutional algorithms have been developed and applied . Still, the spatial resolution in both MRI and US is much lower than OCT, making the detection of subtle tissue changes in the early phase of cartilage degeneration practically impossible.…”

Section: Discussionmentioning

confidence: 99%

Coregistered Spectral Optical Coherence Tomography and Two‐Photon Microscopy for Multimodal Near‐Instantaneous Deep‐Tissue Imaging

et al. 2020

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Two-photon microscopy (2PM) has brought unique insight into the mechanisms underlying immune system dynamics and function since it enables monitoring of cellular motility and communication in complex systems within their genuine environment-the living organism. However, use of 2PM in clinical settings is limited. In contrast, optical coherence tomography (OCT), a noninvasive label-free diagnostic imaging method, which allows monitoring morphologic changes of large tissue regions in vivo, has found broad application in the clinic. Here we developed a combined multimodal technology to achieve near-instantaneous coregistered OCT, 2PM, and second harmonic generation (SHG) imaging over large volumes (up to 1,000 × 1,000 × 300 μm 3 ) of tendons and other tissue compartments in mouse paws, as well as in mouse lymph nodes, spleens, and femurs. Using our multimodal imaging approach, we found differences in macrophage cell shape and motility behavior depending on whether they are located in tendons or in the surrounding tissue compartments of the mouse paw. The cellular shape of tissue-resident macrophages, indicative for their role in tissue, correlated with the supramolecular organization of collagen as revealed by SHG and OCT. Hence, the herepresented approach of coregistered OCT and 2PM has the potential to link specific cellular phenotypes and functions (as revealed by 2PM) to tissue morphology (as highlighted by OCT) and thus, to build a bridge between basic research knowledge and clinical observations.

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Section: Discussionmentioning

confidence: 99%

Coregistered Spectral Optical Coherence Tomography and Two‐Photon Microscopy for Multimodal Near‐Instantaneous Deep‐Tissue Imaging

et al. 2020

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“…At present, ultrasound computer-aided diagnosis (USCAD) systems have been developed for multiple clinical applications. 27 The four most studied rheumatic diseases where artificial intelligence, such as machine learning or artificial neural network, has been applied are: RA, OA, systemic lupus erythematosus, and idiopathic inflammatory myopathies. 27 In 2019, Anderson et al 28 showed that neural network technology could be used in the scoring of disease activity on Doppler US images.…”

Section: Update In Ultrasoundmentioning

confidence: 99%

Imaging update in inflammatory arthritis

Sudoł‐Szopińska

Giraudo

Oei

et al. 2021

Journal of Clinical Orthopaedics and Trauma

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Ultrasonography and magnetic resonance imaging have become important imaging modalities in rheumatological disorders next to standard radiography. Due to their ability to assess both morphological and functional changes they play a significant role in early diagnosis and treatment monitoring. This review presents the latest advancements in imaging of inflammatory arthritis with a focus on two main groups of rheumatic diseases: connective tissue diseases and spondyloarthritis. New developments related to peripheral and sacroiliac joints imaging are discussed, including Superb Micro Flow Imaging and Shear Wave Elastography in ultrasonography, as well as Whole Body MRI, quantitative MRI, and the recent advances in cartilage imaging in MRI, including T2-and T1p-mapping, and dGEMRIC. The role of emerging imaging techniques in the early diagnosis of inflammatory arthritis is discussed, including DECT, VIBE, BoneMRI, and pQCT.

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“…Previous ML applications to diagnose rheumatological conditions have used biological samples, such as serum biomarkers and genomic data from synovial tissue [ 17 , 18 ]. ML has also been applied to ultrasound techniques to enable a computer-aided diagnosis, for rheumatoid arthritis, osteoarthritis and systemic lupus erythematosus [ 19 ]. Such approaches continue to be limited by practical considerations, including accessibility, cost and time, in addition to variable accuracy.…”

Section: Introductionmentioning

confidence: 99%

A multicentre validation study of a smartphone application to screen hand arthritis

Reed

Rampono²,

Turner³

et al. 2022

BMC Musculoskelet Disord

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Background Arthritis is a common condition, and the prompt and accurate assessment of hand arthritis in primary care is an area of unmet clinical need. We have previously developed and tested a screening tool combining machine-learning algorithms, to help primary care physicians assess patients presenting with arthritis affecting the hands. The aim of this study was to assess the validity of the screening tool among a number of different Rheumatologists. Methods Two hundred and forty-eight consecutive new patients presenting to 7 private Rheumatology practices across Australia were enrolled. Using a smartphone application, each patient had photographs taken of their hands, completed a brief 9-part questionnaire, and had a single examination result (wrist irritability) recorded. The Rheumatologist diagnosis was entered following a 45-minute consultation. Multiple machine learning models were applied to both the photographic and survey/examination results, to generate a screening outcome for the primary diagnoses of osteoarthritis, rheumatoid and psoriatic arthritis. Results The combined algorithms in the application performed well in identifying and discriminating between different forms of hand arthritis. The algorithms were able to predict rheumatoid arthritis with accuracy, precision, recall and specificity of 85.1, 80.0, 88.1 and 82.7% respectively. The corresponding results for psoriatic arthritis were 95.2, 76.9, 90.9 and 95.8%, and for osteoarthritis were 77.4, 78.3, 80.6 and 73.7%. The results were maintained when each contributor was excluded from the analysis. The median time to capture all data across the group was 2 minutes and 59 seconds. Conclusions This multicentre study confirms the results of the pilot study, and indicates that the performance of the screening tool is maintained across a group of different Rheumatologists. The smartphone application can provide a screening result from a combination of machine-learning algorithms applied to hand images and patient symptom responses. This could be used to assist primary care physicians in the assessment of patients presenting with hand arthritis, and has the potential to improve the clinical assessment and management of such patients.

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Computer-aided diagnosis in rheumatic diseases using ultrasound: an overview

Cited by 11 publications

References 57 publications

Coregistered Spectral Optical Coherence Tomography and Two‐Photon Microscopy for Multimodal Near‐Instantaneous Deep‐Tissue Imaging

Coregistered Spectral Optical Coherence Tomography and Two‐Photon Microscopy for Multimodal Near‐Instantaneous Deep‐Tissue Imaging

Imaging update in inflammatory arthritis

A multicentre validation study of a smartphone application to screen hand arthritis

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