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

Contrast-enhanced harmonic endoscopic ultrasound (CH-EUS) MASTER: A novel deep learning-based system in pancreatic mass diagnosis

BACKGROUND AND AIMS

Distinguishing pancreatic cancer from nonneoplastic masses is critical and remains a clinical challenge. The study aims to construct a deep learning-based artificial intelligence system to facilitate pancreatic mass diagnosis, and to guide EUS-guided fine-needle aspiration (EUS-FNA) in real time.

METHODS

This is a prospective study. The CH-EUS MASTER system is composed of Model 1 (real-time capture and segmentation) and Model 2 (benign and malignant identification). It was developed using deep convolutional neural networks and Random Forest algorithm. Patients with pancreatic masses undergoing CH-EUS examinations followed by EUS-FNA were recruited. All patients underwent CH-EUS and were diagnosed both by endoscopists and CH-EUS MASTER. After diagnosis, they were randomly assigned to undergo EUS-FNA with or without CH-EUS MASTER guidance.

RESULTS

Compared with manual labeling by experts, the average overlap rate of Model 1 was 0.708. In the independent CH-EUS video testing set, Model 2 generated an accuracy of 88.9% in identifying malignant tumors. In clinical trial, the accuracy, sensitivity, and specificity for diagnosing pancreatic masses by CH-EUS MASTER were significantly better than that of endoscopists. The accuracy, sensitivity, specificity, positive predictive value, and negative predictive value were respectively 93.8%, 90.9%, 100%, 100%, and 83.3% by CH-EUS MASTER guided EUS-FNA, and were not significantly different compared to the control group. CH-EUS MASTER-guided EUS-FNA significantly improved the first-pass diagnostic yield.

CONCLUSION

CH-EUS MASTER is a promising artificial intelligence system diagnosing malignant and benign pancreatic masses and may guide FNA in real time.

TRIAL REGISTRATION NUMBER

NCT04607720.

Correlations between B-mode ultrasound image texture features and tissue temperatures in hyperthermia

PURPOSE

The noninvasive monitoring of mild hyperthermia or thermal ablation is important to guarantee therapeutic safety and efficacy. The potential of ultrasound B-mode image texture features in monitoring temperature or coagulation zones studied in this article.

MATERIALS AND METHODS

The experiments carried out on eighteen in vitro porcine liver samples heated from 20°C to 60°C in the water bath. The ultrasound radiofrequency signal at different temperature collected to reconstruct B-mode ultrasound images. The texture features based on gray level histogram (GLH), gray level co-occurrence matrix (GLCM), and gray level-gradient co-occurrence matrix (GGCM) extracted, respectively. Accordingly, we analyze the correlations between these texture features and temperature based on the experiment results.

RESULTS

The results showed that five texture feature parameters closely related to temperature, including mean gray scale of GLH, homogeneity of GLCM, hybrid entropy, inverse difference moment, and correlation of GGCM. Some of these feature parameters have correlation coefficients larger than 0.9 within the temperature range of 20°C to 60°C.

CONCLUSIONS

The above-mentioned five feature parameters expected to apply for noninvasive monitoring of MH or TA.

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

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.

Machine learning approaches to improve disease management of patients with rheumatoid arthritis: review and future directions

INTRODUCTION

Although the management of rheumatoid arthritis (RA) has improved in major way over the last decades, this disease still leads to an important burden for patients and society, and there is a need to develop more personalized approaches. Machine learning (ML) methods are more and more used in health-related studies and can be applied to different sorts of data (clinical, radiological, or 'omics' data). Such approaches may improve the management of patients with RA.

AREAS COVERED

In this paper, we propose a review regarding ML approaches applied to RA. A scoping literature search was performed in PubMed, in September 2021 using the following MeSH terms: 'arthritis, rheumatoid' and 'machine learning'. Based on this search, the usefulness of ML methods for RA diagnosis, monitoring, and prediction of response to treatment and RA outcomes, is discussed.

EXPERT OPINION

ML methods have the potential to revolutionize RA-related research and improve disease management and patient care. Nevertheless, these models are not yet ready to contribute fully to rheumatologists' daily practice. Indeed, these methods raise technical, methodological, and ethical issues, which should be addressed properly to allow their implementation. Collaboration between data scientists, clinical researchers, and physicians is therefore required to move this field forward.

Imaging update in inflammatory arthritis

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.

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

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 μm³ ) 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 here-presented 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. © 2020 The Authors. Cytometry Part A published by Wiley Periodicals, Inc. on behalf of International Society for Advancement of Cytometry.