Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Imaging in clinical trials for psoriatic arthritis: a scoping review

Psoriatic arthritis (PsA) is a chronic inflammatory condition principally affecting the skin and musculoskeletal system, associated with comorbidities and decreased quality of life. Imaging is crucial for diagnosis, monitoring progression, and evaluating treatment efficacy; therefore, it plays an important role in PsA clinical trials. In this review, we aimed to characterize how imaging modalities and advances in imaging techniques are being used specifically in the PsA clinical trial literature. Following the Arksey and O'Malley framework for scoping reviews, we conducted a comprehensive search of multiple literature databases, from January 1, 2000, to June 27, 2024. We captured 1961 articles and after deduplication, title and abstract screening, and full-text review, 53 studies were included. Radiographs were the most used imaging modality (n = 32/53, 60%), and radiographic progression was frequently measured using the PsA-modified Sharp/van der Heijde score. MRI (n = 16/53, 30%) was used in evaluating peripheral and axial disease, with more recent adoption of validated scoring systems (PsAMRIS and SPARCC). Ultrasound (n = 11/53, 21%), including power Doppler, was used to assess soft tissue inflammation. Standardized scoring systems (e.g. GLOESS) were used in a minority of ultrasound-based studies. Multimodality imaging (n = 7/53, 13%) and CT (n = 2/53, 4%) was uncommon. The development of PsA-specific scoring systems for radiographs and MRI has been instrumental in advancing imaging assessment in PsA. However, their application remains limited, particularly in ultrasound, where further standardization is needed. Future clinical trials should focus on increasing the adoption of PsA-specific scoring systems, exploring advanced techniques (e.g. DCE-MRI), and multi-modal imaging to improve PsA disease monitoring.

The role of deep learning in diagnostic imaging of spondyloarthropathies: a systematic review

AIM

Diagnostic imaging is an integral part of identifying spondyloarthropathies (SpA), yet the interpretation of these images can be challenging. This review evaluated the use of deep learning models to enhance the diagnostic accuracy of SpA imaging.

METHODS

Following PRISMA guidelines, we systematically searched major databases up to February 2024, focusing on studies that applied deep learning to SpA imaging. Performance metrics, model types, and diagnostic tasks were extracted and analyzed. Study quality was assessed using QUADAS-2.

RESULTS

We analyzed 21 studies employing deep learning in SpA imaging diagnosis across MRI, CT, and X-ray modalities. These models, particularly advanced CNNs and U-Nets, demonstrated high accuracy in diagnosing SpA, differentiating arthritis forms, and assessing disease progression. Performance metrics frequently surpassed traditional methods, with some models achieving AUCs up to 0.98 and matching expert radiologist performance.

CONCLUSION

This systematic review underscores the effectiveness of deep learning in SpA imaging diagnostics across MRI, CT, and X-ray modalities. The studies reviewed demonstrated high diagnostic accuracy. However, the presence of small sample sizes in some studies highlights the need for more extensive datasets and further prospective and external validation to enhance the generalizability of these AI models.

KEY POINTS

Question How can deep learning models improve diagnostic accuracy in imaging for spondyloarthropathies (SpA), addressing challenges in early detection and differentiation from other forms of arthritis? Findings Deep learning models, especially CNNs and U-Nets, showed high accuracy in SpA imaging across MRI, CT, and X-ray, often matching or surpassing expert radiologists. Clinical relevance Deep learning models can enhance diagnostic precision in SpA imaging, potentially reducing diagnostic delays and improving treatment decisions, but further validation on larger datasets is required for clinical integration.

AI-Enhanced Detection of Clinically Relevant Structural and Functional Anomalies in MRI: Traversing the Landscape of Conventional to Explainable Approaches

Anomaly detection in medical imaging, particularly within the realm of magnetic resonance imaging (MRI), stands as a vital area of research with far-reaching implications across various medical fields. This review meticulously examines the integration of artificial intelligence (AI) in anomaly detection for MR images, spotlighting its transformative impact on medical diagnostics. We delve into the forefront of AI applications in MRI, exploring advanced machine learning (ML) and deep learning (DL) methodologies that are pivotal in enhancing the precision of diagnostic processes. The review provides a detailed analysis of preprocessing, feature extraction, classification, and segmentation techniques, alongside a comprehensive evaluation of commonly used metrics. Further, this paper explores the latest developments in ensemble methods and explainable AI, offering insights into future directions and potential breakthroughs. This review synthesizes current insights, offering a valuable guide for researchers, clinicians, and medical imaging experts. It highlights AI's crucial role in improving the precision and speed of detecting key structural and functional irregularities in MRI. Our exploration of innovative techniques and trends furthers MRI technology development, aiming to refine diagnostics, tailor treatments, and elevate patient care outcomes. LEVEL OF EVIDENCE: 5 TECHNICAL EFFICACY: Stage 1.

Treatment controversies in spondyloarthritis and psoriatic arthritis: focus on biologics and targeted therapies

INTRODUCTION

There are several treatment controversies that have emerged in spondyloarthritis and psoriatic arthritis. These are related to the nature of the conditions as well as to the use of medications.

AREAS COVERED

This review, which included a search of PubMed database as well as the references within the articles provides an overview of the nature of spondyloarthritis, controversy over the inclusion of psoriatic arthritis (PsA) as a peripheral spondyloarthritis, and a summary of current treatments for both PsA and axial spondyloarthritis (axSpA), with special emphasis on targeted therapy. The review highlights the differences in response to certain medications, particularly biologic therapy and summarizes the randomized controlled trials in psoriatic arthritis and axial spondyloarthritis providing data about the responses in table format.

EXPERT OPINION

There is a need for better outcome measures in axSpA. Currently, the measures are subjective. Imaging may be more appropriate but there is a need for research into the reliability and responsiveness of imaging techniques. In PsA, there may also be better response measures and research into the reliability and responsiveness of available measures is underway. There is also a need for novel therapies as well as biomarkers for response in both diseases.

Reliability of a generative artificial intelligence tool for pediatric familial Mediterranean fever: insights from a multicentre expert survey

BACKGROUND

Artificial intelligence (AI) has become a popular tool for clinical and research use in the medical field. The aim of this study was to evaluate the accuracy and reliability of a generative AI tool on pediatric familial Mediterranean fever (FMF).

METHODS

Fifteen questions repeated thrice on pediatric FMF were prompted to the popular generative AI tool Microsoft Copilot with Chat-GPT 4.0. Nine pediatric rheumatology experts rated response accuracy with a blinded mechanism using a Likert-like scale with values from 1 to 5.

RESULTS

Median values for overall responses at the initial assessment ranged from 2.00 to 5.00. During the second assessment, median values spanned from 2.00 to 4.00, while for the third assessment, they ranged from 3.00 to 4.00. Intra-rater variability showed poor to moderate agreement (intraclass correlation coefficient range: -0.151 to 0.534). A diminishing level of agreement among experts over time was documented, as highlighted by Krippendorff's alpha coefficient values, ranging from 0.136 (at the first response) to 0.132 (at the second response) to 0.089 (at the third response). Lastly, experts displayed varying levels of trust in AI pre- and post-survey.

CONCLUSIONS

AI has promising implications in pediatric rheumatology, including early diagnosis and management optimization, but challenges persist due to uncertain information reliability and the lack of expert validation. Our survey revealed considerable inaccuracies and incompleteness in AI-generated responses regarding FMF, with poor intra- and extra-rater reliability. Human validation remains crucial in managing AI-generated medical information.

Deep learning-based classification of erosion, synovitis and osteitis in hand MRI of patients with inflammatory arthritis

OBJECTIVES

To train, test and validate the performance of a convolutional neural network (CNN)-based approach for the automated assessment of bone erosions, osteitis and synovitis in hand MRI of patients with inflammatory arthritis.

METHODS

Hand MRIs (coronal T1-weighted, T2-weighted fat-suppressed, T1-weighted fat-suppressed contrast-enhanced) of rheumatoid arthritis (RA) and psoriatic arthritis (PsA) patients from the rheumatology department of the Erlangen University Hospital were assessed by two expert rheumatologists using the Outcome Measures in Rheumatology-validated RA MRI Scoring System and PsA MRI Scoring System scores and were used to train, validate and test CNNs to automatically score erosions, osteitis and synovitis. Scoring performance was compared with human annotations in terms of macro-area under the receiver operating characteristic curve (AUC) and balanced accuracy using fivefold cross-validation. Validation was performed on an independent dataset of MRIs from a second patient cohort.

RESULTS

In total, 211 MRIs from 112 patients (14 906 region of interests (ROIs)) were included for training/internal validation using cross-validation and 220 MRIs from 75 patients (11 040 ROIs) for external validation of the networks. The networks achieved high mean (SD) macro-AUC of 92%±1% for erosions, 91%±2% for osteitis and 85%±2% for synovitis. Compared with human annotation, CNNs achieved a high mean Spearman correlation for erosions (90±2%), osteitis (78±8%) and synovitis (69±7%), which remained consistent in the validation dataset.

CONCLUSIONS

We developed a CNN-based automated scoring system that allowed a rapid grading of erosions, osteitis and synovitis with good diagnostic accuracy and using less MRI sequences compared with conventional scoring. This CNN-based approach may help develop standardised cost-efficient and time-efficient assessments of hand MRIs for patients with arthritis.

Implementation of artificial intelligence models in magnetic resonance imaging with focus on diagnosis of rheumatoid arthritis and axial spondyloarthritis: narrative review

Early diagnosis in rheumatoid arthritis (RA) and axial spondyloarthritis (axSpA) is essential to initiate timely interventions, such as medication and lifestyle changes, preventing irreversible joint damage, reducing symptoms, and improving long-term outcomes for patients. Since magnetic resonance imaging (MRI) of the wrist and hand, in case of RA and MRI of the sacroiliac joints (SIJ) in case of axSpA can identify inflammation before it is clinically discernible, this modality may be crucial for early diagnosis. Artificial intelligence (AI) techniques, together with machine learning (ML) and deep learning (DL) have quickly evolved in the medical field, having an important role in improving diagnosis, prognosis, in evaluating the effectiveness of treatment and monitoring the activity of rheumatic diseases through MRI. The improvements of AI techniques in the last years regarding imaging interpretation have demonstrated that a computer-based analysis can equal and even exceed the human eye. The studies in the field of AI have investigated how specific algorithms could distinguish between tissues, diagnose rheumatic pathology and grade different signs of early inflammation, all of them being crucial for tracking disease activity. The aim of this paper is to highlight the implementation of AI models in MRI with focus on diagnosis of RA and axSpA through a literature review.