Machine learning-based prediction of radiographic progression in patients with axial spondyloarthritis

Imaging in clinical trials of axial spondyloarthritis: what type of imaging should be used

Axial spondyloarthritis (axSpA) is a chronic inflammatory condition predominantly affecting the sacroiliac joints and spine. Early and accurate diagnosis is crucial to prevent structural damage and improve patient outcomes. Imaging plays a pivotal role in axSpA diagnosis, monitoring, and clinical trials, offering insights into both inflammatory activity and structural progression. Conventional radiography has been foundational for detecting structural changes, such as syndesmophytes and erosions, but it is limited by poor sensitivity for early disease detection and significant interobserver variability. Advanced imaging modalities, such as magnetic resonance imaging (MRI) and low-dose computed tomography (ld-CT), have emerged as more sensitive tools. MRI excels in identifying active inflammation, particularly bone marrow edema, and is integral to early diagnosis and disease monitoring. ld-CT provides superior spatial resolution for detecting structural lesions while minimizing radiation exposure. However, challenges remain in achieving standardized imaging protocols and consistent scoring systems across clinical trials. Scoring systems like the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS), Spondyloarthritis Research Consortium of Canada (SPARCC) scores, and Berlin methods require rigorous calibration to ensure reliability. The purpose of this review is to explore the strengths and limitations as well as the use in clinical trials of the different imaging modalities and to offer guidance on selecting the most suitable imaging techniques for assessing both disease activity and structural progression in clinical trials.

Deep learning analysis for rheumatologic imaging: current trends, future directions, and the role of human

Rheumatic diseases, such as rheumatoid arthritis (RA), osteoarthritis (OA), and spondyloarthritis (SpA), present diagnostic and management challenges due to their impact on connective tissues and the musculoskeletal system. Traditional imaging techniques, including plain radiography, ultrasounds, computed tomography, and magnetic resonance imaging (MRI), play a critical role in diagnosing and monitoring these conditions, but face limitations like inter-observer variability and time-consuming assessments. Recently, deep learning (DL), a subset of artificial intelligence, has emerged as a promising tool for enhancing medical imaging analysis. Convolutional neural networks, a DL model type, have shown great potential in medical image classification, segmentation, and anomaly detection, often surpassing human performance in tasks like tumor identification and disease severity grading. In rheumatology, DL models have been applied to plain radiography, ultrasounds, and MRI for assessing joint damage, synovial inflammation, and disease progression in RA, OA, and SpA patients. Despite the promise of DL, challenges such as data bias, limited explainability, and the need for large annotated datasets remain significant barriers to its widespread adoption. Furthermore, human oversight and value judgment are essential for ensuring the ethical use and effective implementation of DL in clinical settings. This review provides a comprehensive overview of DL's applications in rheumatologic imaging and explores its future potential in enhancing diagnosis, treatment decisions, and personalized medicine.

Current Applications and Future Implications of Artificial Intelligence in Spine Surgery and Research: A Narrative Review and Commentary

STUDY DESIGN

Narrative review.

OBJECTIVES

Artificial intelligence (AI) is being increasingly applied to the domain of spine surgery. We present a review of AI in spine surgery, including its use across all stages of the perioperative process and applications for research. We also provide commentary regarding future ethical considerations of AI use and how it may affect surgeon-industry relations.

METHODS

We conducted a comprehensive literature review of peer-reviewed articles that examined applications of AI during the pre-, intra-, or postoperative spine surgery process. We also discussed the relationship among AI, spine industry partners, and surgeons.

RESULTS

Preoperatively, AI has been mainly applied to image analysis, patient diagnosis and stratification, decision-making. Intraoperatively, AI has been used to aid image guidance and navigation. Postoperatively, AI has been used for outcomes prediction and analysis. AI can enable curation and analysis of huge datasets that can enhance research efforts. Large amounts of data are being accrued by industry sources for use by their AI platforms, though the inner workings of these datasets or algorithms are not well known.

CONCLUSIONS

AI has found numerous uses in the pre-, intra-, or postoperative spine surgery process, and the applications of AI continue to grow. The clinical applications and benefits of AI will continue to be more fully realized, but so will certain ethical considerations. Making industry-sponsored databases open source, or at least somehow available to the public, will help alleviate potential biases and obscurities between surgeons and industry and will benefit patient care.

Precision medicine in axial spondyloarthritis: current opportunities and future perspectives

Axial spondyloarthritis (axSpA) is a complex disease characterized by a diverse range of clinical presentations. The primary manifestation is inflammatory lower back pain, often accompanied by other clinical manifestations such as peripheral arthritis, enthesitis, uveitis, psoriasis, and inflammatory bowel disease. However, the presentation of axSpA can vary widely among patients. Despite extensive research, the precise pathogenesis of axSpA remains largely unknown. The lack of complete understanding poses challenges in subgrouping the disease, developing specific treatment approaches, and predicting treatment response. In this review, we will explore the limitations in diagnosing and treating axSpA. In addition, we will examine the current knowledge and potential opportunities provided by various omics and technological advancements in enhancing the diagnosis and personalized treatment of axSpA.

Large language models and rheumatology: are we there yet?

The last 2 years have marked the beginning of a golden age for natural language processing in medicine. The arrival of large language models (LLMs) and multimodal models have raised new opportunities and challenges for research and clinical practice. In rheumatology, a specialty rich in data and requiring complex decision-making, the use of these tools may transform diagnostic procedures, improve patient interaction and simplify data management, leading to more personalized and efficient healthcare outcomes. The objective of this article is to present an overview of the status of LLMs in the field of rheumatology while discussing some of the challenges ahead in this area of great potential.

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.

Ankylosing spondylitis prediction using fuzzy K-nearest neighbor classifier assisted by modified JAYA optimizer

The diagnosis of ankylosing spondylitis (AS) can be complex, necessitating a comprehensive assessment of medical history, clinical symptoms, and radiological evidence. This multidimensional approach can exacerbate the clinical burden and increase the likelihood of diagnostic inaccuracies, which may result in delayed or overlooked cases. Consequently, supplementary diagnostic techniques for AS have become a focal point in clinical research. This study introduces an enhanced optimization algorithm, SCJAYA, which incorporates salp swarm foraging behavior with cooperative predation strategies into the JAYA algorithm framework, noted for its robust optimization capabilities that emulate the evolutionary dynamics of biological organisms. The integration of salp swarm behavior is aimed at accelerating the convergence speed and enhancing the quality of solutions of the classical JAYA algorithm while the cooperative predation strategy is incorporated to mitigate the risk of convergence on local optima. SCJAYA has been evaluated across 30 benchmark functions from the CEC2014 suite against 9 conventional meta-heuristic algorithms as well as 9 state-of-the-art meta-heuristic counterparts. The comparative analyses indicate that SCJAYA surpasses these algorithms in terms of convergence speed and solution precision. Furthermore, we proposed the bSCJAYA-FKNN classifier: an advanced model applying the binary version of SCJAYA for feature selection, with the aim of improving the accuracy in diagnosing and prognosticating AS. The efficacy of the bSCJAYA-FKNN model was substantiated through validation on 11 UCI public datasets in addition to an AS-specific dataset. The model exhibited superior performance metrics-achieving an accuracy rate, specificity, Matthews correlation coefficient (MCC), F-measure, and computational time of 99.23 %, 99.52 %, 0.9906, 99.41 %, and 7.2800 s, respectively. These results not only underscore its profound capability in classification but also its substantial promise for the efficient diagnosis and prognosis of AS.

Machine learning models with time-series clinical features to predict radiographic progression in patients with ankylosing spondylitis

Objective

Ankylosing spondylitis (AS) is chronic inflammatory arthritis causing structural damage and radiographic progression to the spine due to repeated and continuous inflammation over a long period. This study establishes the application of machine learning models to predict radiographic progression in AS patients using time-series data from electronic medical records (EMRs).

Methods

EMR data, including baseline characteristics, laboratory findings, drug administration, and modified Stoke AS Spine Score (mSASSS), were collected from 1,123 AS patients between January 2001 and December 2018 at a single center at the time of first (T1), second (T2), and third (T3) visits. The radiographic progression of the (n+1)th visit (Pn+1=(mSASSSn+1-mSASSSn)/(Tn+1-Tn)≥1 unit per year) was predicted using follow-up visit datasets from T1 to Tn. We used three machine learning methods (logistic regression with the least absolute shrinkage and selection operation, random forest, and extreme gradient boosting algorithms) with three-fold cross-validation.

Results

The random forest model using the T1 EMR dataset best predicted the radiographic progression P2 among the machine learning models tested with a mean accuracy and area under the curves of 73.73% and 0.79, respectively. Among the T1 variables, the most important variables for predicting radiographic progression were in the order of total mSASSS, age, and alkaline phosphatase.

Conclusion

Prognosis predictive models using time-series data showed reasonable performance with clinical features of the first visit dataset when predicting radiographic progression.

T1-weighted images-based radiomics for structural lesions evaluation in patients with suspected axial spondyloarthritis

PURPOSE

The aim of this study was to investigate the feasibility of radiomics based on T1-weighted images (T1WI) for assessing sacroiliac joint (SIJ) structural lesions in patients with suspected axial spondyloarthritis (axSpA).

MATERIALS AND METHODS

A total of 266 patients with clinical suspicion of axSpA between December 2016 and January 2022 were enrolled. Structural lesions were assessed on low-dose CT (ldCT) and MRI, respectively. Radiomic features, extracted from SIJ T1WI, were included to generate the radiomics model. The performance of the radiomics model was evaluated using receiver operating characteristic (ROC) curve. Furthermore, point-biserial correlation analysis was used to interpret the associations between the radiomic feature and structural lesions.

RESULTS

Using ldCT as the reference standard, the radiomics model showed favorable performance for detecting positive global structural lesions in the training cohort (AUC, 0.82 [95% CI: 0.76, 0.88]) and validation cohort (AUC, 0.82 [95% CI: 0.72, 0.91]. Experienced MRI raters yielded predictive AUCs of 0.73 (95% CI: 0.67, 0.79), and 0.74 (95% CI: 0.66, 0.83) in the training and validation cohort, respectively. The seven radiomic features included in the radiomics model showed significant correlation with different kinds of structural lesions (P all < 0.05). Among them, Wavelet.LHL_firstorder_90Percentile showed the strongest association with fat lesion (r = 0.48, P < 0.05).

CONCLUSION

The radiomics analysis with T1WI could effectively detect SIJ structural lesions and achieved expert-level performance. Each radiomic feature was correlated with different structural lesions significantly, which might inform radiomic-based applications for axSpA intelligent diagnosis.

Quantitative prediction of radiographic progression in patients with axial spondyloarthritis using neural network model in a real-world setting

BACKGROUND

Predicting radiographic progression in axial spondyloarthritis (axSpA) remains limited because of the complex interaction between multiple associated factors and individual variability in real-world settings. Hence, we tested the feasibility of artificial neural network (ANN) models to predict radiographic progression in axSpA.

METHODS

In total, 555 patients with axSpA were split into training and testing datasets at a 3:1 ratio. A generalized linear model (GLM) and ANN models were fitted based on the baseline clinical characteristics and treatment-dependent variables for the modified Stoke Ankylosing Spondylitis Spine Score (mSASSS) of the radiographs at follow-up time points. The mSASSS prediction was evaluated, and explainable machine learning methods were used to provide insights into the model outcome or prediction.

RESULTS

The R² values of the fitted models were in the range of 0.90-0.95 and ANN with an input of mSASSS as the number of each score performed better (root mean squared error (RMSE) = 2.83) than GLM or input of mSASSS as a total score (RMSE = 2.99-3.57). The ANN also effectively captured complex interactions among variables and their contributions to the transition of mSASSS over time in the fitted models. Structural changes constituting the mSASSS scoring systems were the most important contributing factors, and no detectable structural abnormalities at baseline were the most significant factors suppressing mSASSS change.

CONCLUSIONS

Clinical and radiographic data-driven ANN allows precise mSASSS prediction in real-world settings. Correct evaluation and prediction of spinal structural changes could be beneficial for monitoring patients with axSpA and developing a treatment plan.

Current Applications of Machine Learning in Spine: From Clinical View

STUDY DESIGN

Narrative review.

OBJECTIVES

This review aims to present current applications of machine learning (ML) in spine domain to clinicians.

METHODS

We conducted a comprehensive PubMed search of peer-reviewed articles that were published between 2006 and 2020 using terms (spine, spinal, lumbar, cervical, thoracic, machine learning) to examine ML in spine. Then exclude research of other domain, case report, review or meta-analysis, and which without available abstract or full text.

RESULTS

Total 1738 articles were retrieved from database, and 292 studies were finally included. Key findings of current applications were compiled and summarized in this review. Main clinical applications of those techniques including image processing, diagnosis, decision supporting, operative assistance, rehabilitation, surgery outcomes, complications, hospitalization and cost.

CONCLUSIONS

ML had achieved excellent performance and hold immense potential in spine. ML could help clinical staff to improve medical level, enhance work efficiency, and reduce adverse events. However more randomized controlled trials and improvement of interpretability are essential to clinicians accepting models' assistance in real work.

Lifestyle modification and inflammation in people with axial spondyloarthropathy-A scoping review

INTRODUCTION

People with axial spondyloarthritis (AS) have an inflammatory profile, increasing the risk of hypertension, type 2 diabetes, obesity, and dyslipidaemia. Consequently, AS is linked with co-morbidities such as cardiovascular disease (CVD). Physical inactivity, diet, smoking, alcohol consumption, and obesity influence inflammation, but knowledge of the interaction between these with inflammation, disease activity, and CVD risk in AS is dominated by cross-sectional research.

METHODS

A review of the literature was conducted between July 2020 and December 2021. The focus of the scoping review is to summarise longitudinal and randomised control trials in humans to investigate how tracking or modifying lifestyle influences inflammation and disease burden in patients with AS.

KEY MESSAGES

(1) Lifestyle modifications, especially increased physical activity (PA), exercise, and smoking cessation, are critical in managing AS. (2) Smoking is negatively associated with patient reported outcome measures with AS, plus pharmaceutical treatment adherence, but links with structural radiographic progression are inconclusive. (3) Paucity of data warrant structured studies measuring inflammatory cytokine responses to lifestyle modification in AS.

CONCLUSION

Increased PA, exercise, and smoking cessation should be supported at every given opportunity to improve health outcomes in patients with AS. The link between smoking and radiographic progression needs further investigation. Studies investigating the longitudinal effect of body weight, alcohol, and psychosocial factors on disease activity and physical function in patients with AS are needed. Given the link between inflammation and AS, future studies should also incorporate markers of chronic inflammation beyond the standard C-reactive protein and erythrocyte sedimentation rate measurements.

Artificial intelligence in spine care: current applications and future utility

PURPOSE

The field of artificial intelligence is ever growing and the applications of machine learning in spine care are continuously advancing. Given the advent of the intelligence-based spine care model, understanding the evolution of computation as it applies to diagnosis, treatment, and adverse event prediction is of great importance. Therefore, the current review sought to synthesize findings from the literature at the interface of artificial intelligence and spine research.

METHODS

A narrative review was performed based on the literature of three databases (MEDLINE, CINAHL, and Scopus) from January 2015 to March 2021 that examined historical and recent advancements in the understanding of artificial intelligence and machine learning in spine research. Studies were appraised for their role in, or description of, advancements within image recognition and predictive modeling for spinal research. Only English articles that fulfilled inclusion criteria were ultimately incorporated in this review.

RESULTS

This review briefly summarizes the history and applications of artificial intelligence and machine learning in spine. Three basic machine learning training paradigms: supervised learning, unsupervised learning, and reinforced learning are also discussed. Artificial intelligence and machine learning have been utilized in almost every facet of spine ranging from localization and segmentation techniques in spinal imaging to pathology specific algorithms which include but not limited to; preoperative risk assessment of postoperative complications, screening algorithms for patients at risk of osteoporosis and clustering analysis to identify subgroups within adolescent idiopathic scoliosis. The future of artificial intelligence and machine learning in spine surgery is also discussed with focusing on novel algorithms, data collection techniques and increased utilization of automated systems.

CONCLUSION

Improvements to modern-day computing and accessibility to various imaging modalities allow for innovative discoveries that may arise, for example, from management. Given the imminent future of AI in spine surgery, it is of great importance that practitioners continue to inform themselves regarding AI, its goals, use, and progression. In the future, it will be critical for the spine specialist to be able to discern the utility of novel AI research, particularly as it continues to pervade facets of everyday spine surgery.

Development of a radiographic scoring system for new bone formation in gout

Background

Features of new bone formation (NBF) are common in tophaceous gout. The aim of this project was to develop a plain radiographic scoring system for NBF in gout.

Methods

Informed by a literature review, scoring systems were tested in 80 individual 1st and 5th metatarsophalangeal joints. Plain radiography scores were compared with computed tomography (CT) measurements of the same joints. The best-performing scoring system was then tested in paired sets of hand and foot radiographs obtained over 2 years from an additional 25 patients. Inter-reader reproducibility was assessed using intraclass correlation coefficients (ICC). NBF scores were correlated with plain radiographic erosion scores (using the gout-modified Sharp-van der Heijde system).

Results

Following a series of structured reviews of plain radiographs and scoring exercises, a semi-quantitative scoring system for sclerosis and spur was developed. In the individual joint analysis, the inter-observer ICC (95% CI) was 0.84 (0.76–0.89) for sclerosis and 0.81 (0.72–0.87) for spur. Plain radiographic sclerosis and spur scores correlated with CT measurements (r = 0.65–0.74, P < 0.001 for all analyses). For the hand and foot radiograph sets, the inter-observer ICC (95% CI) was 0.94 (0.90–0.98) for sclerosis score and 0.76 (0.65–0.84) for spur score. Sclerosis and spur scores correlated highly with plain radiographic erosion scores (r = 0.87 and 0.71 respectively), but not with change in erosion scores over 2 years (r = −0.04–0.15).

Conclusion

A semi-quantitative plain radiographic scoring method for the assessment of NBF in gout is feasible, valid, and reproducible. This method may facilitate consistent measurement of NBF in gout.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13075-021-02683-9.

An introduction to machine learning and analysis of its use in rheumatic diseases

Machine learning (ML) is a computerized analytical technique that is being increasingly employed in biomedicine. ML often provides an advantage over explicitly programmed strategies in the analysis of multidimensional information by recognizing relationships in the data that were not previously appreciated. As such, the use of ML in rheumatology is increasing, and numerous studies have employed ML to classify patients with rheumatic autoimmune inflammatory diseases (RAIDs) from medical records and imaging, biometric or gene expression data. However, these studies are limited by sample size, the accuracy of sample labelling, and absence of datasets for external validation. In addition, there is potential for ML models to overfit or underfit the data and, thereby, these models might produce results that cannot be replicated in an unrelated dataset. In this Review, we introduce the basic principles of ML and discuss its current strengths and weaknesses in the classification of patients with RAIDs. Moreover, we highlight the successful analysis of the same type of input data (for example, medical records) with different algorithms, illustrating the potential plasticity of this analytical approach. Altogether, a better understanding of ML and the future application of advanced analytical techniques based on this approach, coupled with the increasing availability of biomedical data, may facilitate the development of meaningful precision medicine for patients with RAIDs.

Machine Learning in Rheumatic Diseases

With advances in information technology, the demand for using data science to enhance healthcare and disease management is rapidly increasing. Among these technologies, machine learning (ML) has become ubiquitous and indispensable for solving complex problems in many scientific fields, including medical science. ML allows the development of guidelines and framing of the evaluation system for complex diseases based on massive data. In the analysis of rheumatic diseases, which are chronic and remarkably heterogeneous, ML can be anticipated to be extremely helpful in deciphering and revealing the inherent interrelationships in disease development and progression, which can further enhance the overall understanding of the disease, optimize patients' stratification, calibrate therapeutic strategies, and predict prognosis and outcomes. In this review, the basics of ML, its potential clinical applications in rheumatology, together with its strengths and limitations are summarized.

Imaging update in spondyloarthropathy

Although our understanding of axial spondyloarthropathy (axSpA) has increased recently, there has not been a concurrent improvement in patient diagnosis with delays contributing to patient morbidity. Imaging findings of axSpA can be subtle and may be dismissed often due to lack of understanding by reporters and importantly clinicians who do not suspect the disease. Recognition of the importance of imaging has led to the inclusion of MRI as part of the diagnostic criteria for axSpA. With this in mind, a number of advancements have been made in an attempt to increase our diagnostic accuracy on imaging. This article will give an overview of these techniques as well as a recap of the imaging features of axSpA.

Molecular Imaging of Inflammatory Disease

Inflammatory diseases include a wide variety of highly prevalent conditions with high mortality rates in severe cases ranging from cardiovascular disease, to rheumatoid arthritis, to chronic obstructive pulmonary disease, to graft vs. host disease, to a number of gastrointestinal disorders. Many diseases that are not considered inflammatory per se are associated with varying levels of inflammation. Imaging of the immune system and inflammatory response is of interest as it can give insight into disease progression and severity. Clinical imaging technologies such as computed tomography (CT) and magnetic resonance imaging (MRI) are traditionally limited to the visualization of anatomical information; then, the presence or absence of an inflammatory state must be inferred from the structural abnormalities. Improvement in available contrast agents has made it possible to obtain functional information as well as anatomical. In vivo imaging of inflammation ultimately facilitates an improved accuracy of diagnostics and monitoring of patients to allow for better patient care. Highly specific molecular imaging of inflammatory biomarkers allows for earlier diagnosis to prevent irreversible damage. Advancements in imaging instruments, targeted tracers, and contrast agents represent a rapidly growing area of preclinical research with the hopes of quick translation to the clinic.