Artificial intelligence for the detection of sacroiliitis on magnetic resonance imaging in patients with axial spondyloarthritis

Artificial intelligence and machine learning in axial spondyloarthritis

PURPOSE OF REVIEW

To evaluate the current applications and prospects of artificial intelligence and machine learning in diagnosing and managing axial spondyloarthritis (axSpA), focusing on their role in medical imaging, predictive modelling, and patient monitoring.

RECENT FINDINGS

Artificial intelligence, particularly deep learning, is showing promise in diagnosing axSpA assisting with X-ray, computed tomography (CT) and MRI analyses, with some models matching or outperforming radiologists in detecting sacroiliitis and markers. Moreover, it is increasingly being used in predictive modelling of disease progression and personalized treatment, and could aid risk assessment, treatment response and clinical subtype identification. Variable study designs, sample sizes and the predominance of retrospective, single-centre studies still limit the generalizability of results.

SUMMARY

Artificial intelligence technologies have significant potential to advance the diagnosis and treatment of axSpA, providing more accurate, efficient and personalized healthcare solutions. However, their integration into clinical practice requires rigorous validation, ethical and legal considerations, and comprehensive training for healthcare professionals. Future advances in artificial intelligence could complement clinical expertise and improve patient care through improved diagnostic accuracy and tailored therapeutic strategies, but the challenge remains to ensure that these technologies are validated in prospective multicentre trials and ethically integrated into patient care.

An update in the pharmacological management of axial spondyloarthritis

INTRODUCTION

Significant progress has been made in the diagnosis and management of axial spondyloarthritis (AxSpA) over recent decades. A greater understanding of the immunopathogenesis of the disease has paved the way for the development of targeted treatments. Their efficacy has been demonstrated in randomized controlled trials, meta-analyses and one head-to-head study of biologic DMARDs. Treatment decisions in AxSpA are currently influenced by patient choice, co-morbidity, clinician familiarity and cost.

AREAS COVERED

We review the clinical trials that underpin the evidence base for treatments in AxSpA. We also cover the meta-analyses and head-to-head data that seek to support clinicians in personalizing treatment decisions. Further, we discuss the recent international guidelines that provide clinicians with treatment pathways and guidance.

EXPERT OPINION

We conclude that treatment decisions in managing both radiographic and non-radiographic AxSpA should be based on shared decision-making with patients, the clinical effectiveness of drug class, co-morbidity and cost. At present, we have limited head-to-head data to prioritize one drug class over another for first-line treatment but can recommend tumor necrosis factor (TNF), interleukin 17 (IL17) and JAK inhibition as being comparable in terms of clinical, structural and patient-reported outcome measures. Further real-world data may guide treatment decision-making in individual patients.

Advancing Rheumatology Care Through Machine Learning

Rheumatologic diseases are marked by their complexity, involving immune-, metabolic- and mechanically mediated processes which can affect different organ systems. Despite a growing arsenal of targeted medications, many rheumatology patients fail to achieve full remission. Assessing disease activity remains challenging, as patients prioritize different symptoms and disease phenotypes vary. This is also reflected in clinical trials where the efficacy of drugs is not necessarily measured in an optimal way with the traditional outcome assessment. The recent COVID-19 pandemic has catalyzed a digital transformation in healthcare, embracing telemonitoring and patient-reported data via apps and wearables. As a further driver of digital medicine, electronic medical record (EMR) providers are actively engaged in developing algorithms for clinical decision support, heralding a shift towards patient-centered, decentralized care. Machine learning algorithms have emerged as valuable tools for handling the increasing volume of patient data, promising to enhance treatment quality and patient well-being. Convolutional neural networks (CNN) are particularly promising for radiological image analysis, aiding in the detection of specific lesions such as erosions, sacroiliitis, or osteoarthritis, with several FDA-approved applications. Clinical predictions, including numerical disease activity forecasts and medication choices, offer the potential to optimize treatment strategies. Numeric predictions can be integrated into clinical workflows, allowing for shared decision making with patients. Clustering patients based on disease characteristics provides a personalized care approach. Digital biomarkers, such as patient-reported outcomes and wearables data, offer insights into disease progression and therapy response more flexibly and outside patient consultations. In association with patient-reported outcomes, disease-specific digital biomarkers via image recognition or single-camera motion capture enables more efficient remote patient monitoring. Digital biomarkers may also play a major role in clinical trials in the future as continuous, disease-specific outcome measurement facilitating decentralized studies. Prediction models can help with patient selection in clinical trials, such as by predicting high disease activity. Efforts are underway to integrate these advancements into clinical workflows using digital pathways and remote patient monitoring platforms. In summary, machine learning, digital biomarkers, and advanced imaging technologies hold immense promise for enhancing clinical decision support and clinical trials in rheumatology. Effective integration will require a multidisciplinary approach and continued validation through prospective studies.

Radiological Insights into Sacroiliitis: A Narrative Review

Sacroiliitis is the inflammation of the sacroiliac joint, the largest axial joint in the human body, contributing to 25% of lower back pain cases. It can be detected using various imaging techniques like radiography, MRI, and CT scans. Treatments range from conservative methods to invasive procedures. Recent advancements in artificial intelligence offer precise detection of this condition through imaging. Treatment options range from physical therapy and medications to invasive methods like joint injections and surgery. Future management looks promising with advanced imaging, regenerative medicine, and biologic therapies, especially for conditions like ankylosing spondylitis. We conducted a review on sacroiliitis using imaging data from sources like PubMed and Scopus. Only English studies focusing on sacroiliitis's radiological aspects were included. The findings were organized and presented narratively.