Prediction of knee osteoarthritis progression using radiological descriptors obtained from bone texture analysis and Siamese neural networks: data from OAI and MOST cohorts

Radiomics signature of osteoarthritis: Current status and perspective

Osteoarthritis (OA) is one of the fast-growing disability-related diseases worldwide, which has significantly affected the quality of patients' lives and brings about substantial socioeconomic burdens in medical expenditure. There is currently no cure for OA once the bone damage is established. Unfortunately, the existing radiological examination is limited to grading the disease's severity and is insufficient to precisely diagnose OA, detect early OA or predict OA progression. Therefore, there is a pressing need to develop novel approaches in medical image analysis to detect subtle changes for identifying early OA development and rapid progressors. Recently, radiomics has emerged as a unique approach to extracting high-dimensional imaging features that quantitatively characterise visible or hidden information from routine medical images. Radiomics data mining via machine learning has empowered precise diagnoses and prognoses of disease, mainly in oncology. Mounting evidence has shown its great potential in aiding the diagnosis and contributing to the study of musculoskeletal diseases. This paper will summarise the current development of radiomics at the crossroads between engineering and medicine and discuss the application and perspectives of radiomics analysis for OA diagnosis and prognosis.

The translational potential of this article

Radiomics is a novel approach used in oncology, and it may also play an essential role in the diagnosis and prognosis of OA. By transforming medical images from qualitative interpretation to quantitative data, radiomics could be the solution for precise early OA detection, progression tracking, and treatment efficacy prediction. Since the application of radiomics in OA is still in the early stages and primarily focuses on fundamental studies, this review may inspire more explorations and bring more promising diagnoses, prognoses, and management results of OA.

Short-term variations in trabecular bone texture parameters associated to radio-clinical biomarkers improve the prediction of radiographic knee osteoarthritis progression

The present study aims to examine whether the short-term variations in trabecular bone texture (TBT) parameters, combined with a targeted set of clinical and radiographic data, would improve the prediction of long-term radiographic knee osteoarthritis (KOA) progression. Longitudinal (baseline, 24 and 48-month) data, obtained from the Osteoarthritis Initiative cohort, were available for 1352 individuals, with preexisting OA (1 < Kellgren-Lawrence < 4) at baseline. KOA progression was defined as an increase in the medial joint space narrowing score from the 24-months to the 48-months control point. 16 regions of interest were automatically selected from each radiographic knee and analyzed using fractal dimension. Variations from baseline to 24 months in TBT descriptors as well as selected radiographic and clinical readings were calculated. Different logistic regression models were developed to evaluate the progression prediction performance when associating TBT variations with the selected clinical and radiographic readings. The most predictive model was mainly determined using the area under the receiver operating characteristic curve (AUC). The proposed prediction model including short-term variations in TBT parameters, associated with clinical covariates and radiographic scores, improved the capacity of predicting long-term radiographic KOA progression (AUC of 0.739), compared to models based solely on baseline values (AUC of 0.676, p-value < 0.008).

Subchondral bone in knee osteoarthritis: bystander or treatment target?

The subchondral bone is an important structural component of the knee joint relevant for osteoarthritis (OA) incidence and progression once disease is established. Experimental studies have demonstrated that subchondral bone changes are not simply the result of altered biomechanics, i.e., pathologic loading. In fact, subchondral bone alterations have an impact on joint homeostasis leading to articular cartilage loss already early in the disease process. This narrative review aims to summarize the available and emerging imaging techniques used to evaluate knee OA-related subchondral bone changes and their potential role in clinical trials of disease-modifying OA drugs (DMOADs). Radiographic fractal signature analysis has been used to quantify OA-associated changes in subchondral texture and integrity. Cross-sectional modalities such as cone-beam computed tomography (CT), contrast-enhanced cone beam CT, and micro-CT can also provide high-resolution imaging of the subchondral trabecular morphometry. Magnetic resonance imaging (MRI) has been the most commonly used advanced imaging modality to evaluate OA-related subchondral bone changes such as bone marrow lesions and altered trabecular bone texture. Dual-energy X-ray absorptiometry can provide insight into OA-related changes in periarticular subchondral bone mineral density. Positron emission tomography, using physiological biomarkers of subchondral bone regeneration, has provided additional insight into OA pathogenesis. Finally, artificial intelligence algorithms have been developed to automate some of the above subchondral bone measurements. This paper will particularly focus on semiquantitative methods for assessing bone marrow lesions and their utility in identifying subjects at risk of symptomatic and structural OA progression, and evaluating treatment responses in DMOAD clinical trials.

Biomarkers in osteoarthritis: current status and outlook - the FNIH Biomarkers Consortium PROGRESS OA study

Currently, no disease-modifying therapies are approved for osteoarthritis (OA) use. One obstacle to trial success in this field has been our existing endpoints' limited validity and responsiveness. To overcome this impasse, the Foundation for the NIH OA Biomarkers Consortium is focused on investigating biomarkers for a prognostic context of use for subsequent qualification through regulatory agencies. This narrative review describes this activity and the work underway, focusing on the PROGRESS OA study.

Improved Prediction of Knee Osteoarthritis by the Machine Learning Model XGBoost

Objectives

The accurate prediction of osteoarthritis (OA) severity in patients can be helpful to make the proper decision of intervention. This study aims to build up a powerful model to assess predictive risk factors and severity of knee osteoarthritis (KOA) in the clinical scenario.

Methods

A total of 4796 KOA cases and 1205 features were selected by feature selections from the public OA database, Osteoarthritis Initiative (OAI). Six machine learning-based models were constructed and compared for the accuracy of OA prediction. The gradient-boosting decision tree was used to identify important prediction features in the extreme gradient boosting (XGBoost) model. The performance of models was evaluated by F1-score.

Results

Twenty features were determined as predictors for KOA risk and severity, including the subject characteristics, knee symptoms/risk factors and physical exam. The XGBoost model demonstrated 100% prediction accuracy for 54.7% of examined samples, and the remaining 45.3% of samples showed Kellgren and Lawrence (KL) gradings very close to the actual levels. It showed the highest prediction accuracy with an F1-score of 0.553 among the tested six models.

Conclusions

We demonstrate that the XGBoost is the best model for the prediction of KOA severity in the six examined models. In addition, 20 risk features were determined as the essential predictors of KOA, including the physical exam, knee symptoms/risk factors and subject characteristics, which may be useful for the identification of high-risk KOA cases and for making appropriate treatment decisions as well.

Artificial intelligence in knee osteoarthritis: A comprehensive review for 2022

Objective

The aim of this literature review is to yield a comprehensive and exhaustive overview of the existing evidence and up-to-date applications of artificial intelligence for knee osteoarthritis.

Methods

A literature review was performed by using PubMed, Google Scholar, and IEEE databases for articles published in peer-reviewed journals in 2022. The articles focusing on the use of artificial intelligence in diagnosis and prognosis of knee osteoarthritis and accelerating the image acquisition were selected. For each selected study, the code availability, considered number of patients and knees, imaging type, covariates, grading type of osteoarthritis, models, validation approaches, objectives, and results were reviewed.

Results

395 articles were screened, and 35 of them were reviewed. Eight articles were based on diagnosis, six on prognosis prediction, three on classification, three on accelerated image acquisition, and 15 on segmentation of knee osteoarthritis. 57% of the articles used MRI, 26% radiography, 6% MRI together with radiography, 6% ultrasonography, and 6% only clinical data. 23% of the articles made the computer codes available for their study, and 26% used clinical data. External validation and nested cross-validation were used in 17% and 14% of articles, respectively.

Conclusions

The use of artificial intelligence provided a promising potential to enhance the detection and management of knee osteoarthritis. Translating the developed models into clinics is still in the early stages of development. The translation of artificial intelligence models is expected to be further examined in prospective studies to support clinicians in improving routine healthcare practice.

Osteoarthritis year in review 2022: imaging

PURPOSE

This narrative review summarizes original research focusing on imaging in osteoarthritis (OA) published between April 1st 2021 and March 31st 2022. We only considered English publications that were in vivo human studies.

METHODS

The PubMed, Medline, Embase, Scopus, and ISI Web of Science databases were searched for "Osteoarthritis/OA" studies based on the search terms: "Radiography", "Ultrasound/US", "Computed Tomography/CT", "DXA", "Magnetic Resonance Imaging/MRI", "Artificial Intelligence/AI", and "Deep Learning". This review highlights the anatomical focus of research on the structures within the tibiofemoral, patellofemoral, hip, and hand joints. There is also a noted focus on artificial intelligence applications in OA imaging.

RESULTS

Over the last decade, the increasing trend of using open-access large databases has reached a plateau (from 17 to 37). Compositional MRI has had the most prominent use in OA imaging and its biomarkers have been used in the detection of preclinical OA and prediction of OA outcomes. Most noteworthy, there has been an accelerated rate of publications on the implications of artificial intelligence, used in developing prediction models and performing trabecular texture analysis, in OA imaging (from 17 to 154).

CONCLUSIONS

While imaging has maintained its key role in OA research, publication trends have shown an emphasis on the integration of AI. During the past year, MRI has maintained the highest prevalence in usage while US and CT remain as readily available modalities. Finally, there has been a notable uptake in the development and validation of AI techniques used to perform texture analysis and predict OA progression.

Early knee OA definition-what do we know at this stage? An imaging perspective

While criteria for early-stage knee osteoarthritis (OA) in a primary care setting have been proposed, the role of imaging has been limited to radiography using the standard Kellgren-Lawrence classification. Standardized imaging and interpretation are critical with radiographs, yet studies have also shown that even early stages of radiographic OA already demonstrate advanced damage to knee joint tissues such as cartilage, menisci, and bone marrow. Morphological magnetic resonance imaging (MRI) shows degenerative damage earlier than radiographs and definitions for OA using MRI have been published though no accepted definition of early OA based on MRI is currently available. The clinical significance of structural abnormalities has also not been well defined, and the differentiation between normal aging and structural OA development remains a challenge. Compositional MRI of cartilage provides information on biochemical, degenerative changes within the cartilage matrix before cartilage defects occur and when cartilage damage is potentially reversible. Studies have shown that cartilage composition can predict cartilage loss and radiographic OA. However, while this technology is most promising for characterizing early OA it has currently limited clinical application. Better standardization of compositional MRI is required, which is currently work in progress. Finally, there has been renewed interest in computed tomography (CT) for assessing early knee OA as new techniques such as weight bearing and spectral CT are available, which may provide information on joint loading, cartilage, and bone and potentially have a role in better characterizing early OA. In conclusion, while imaging may have a limited role in diagnosing early OA in a primary care setting, there are advanced imaging technologies available, which detect early degeneration and may thus significantly alter management as new therapeutic modalities evolve.

Systematic review of artificial intelligence tack in preventive orthopaedics: is the land coming soon?

PURPOSE

This study aims to describe and assess the current stage of the artificial intelligence (AI) technology integration in preventive orthopaedics of the knee and hip joints.

MATERIALS AND METHODS

The study was conducted in strict compliance with the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement. Literature databases were searched for articles describing the development and validation of AI models aimed at diagnosing knee or hip joint pathologies or predicting their development or course in patients. The quality of the included articles was assessed using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) and QUADAS-AI tools.

RESULTS

56 articles were found that meet all the inclusion criteria. We identified two problems that block the full integration of AI into the routine of an orthopaedic physician. The first of them is related to the insufficient amount, variety and quality of data for training, and validation and testing of AI models. The second problem is the rarity of rational evaluation of models, which is why their real quality cannot always be evaluated.

CONCLUSION

The vastness and relevance of the studied topic are beyond doubt. Qualitative and optimally validated models exist in all four scopes considered. Additional optimization and confirmation of the models' quality on various datasets are the last technical stumbling blocks for creating usable software and integrating them into the routine of an orthopaedic physician.

Use of artificial neural networks in the prognosis of musculoskeletal diseases-a scoping review

To determine the current evidence on artificial neural network (ANN) in prognostic studies of musculoskeletal diseases (MSD) and to assess the accuracy of ANN in predicting the prognosis of patients with MSD. The scoping review was reported under the Preferred Items for Systematic Reviews and the Meta-Analyses extension for Scope Reviews (PRISMA-ScR). Cochrane Library, Embase, Pubmed, and Web of science core collection were searched from inception to January 2023. Studies were eligible if they used ANN to make predictions about MSD prognosis. Variables, model prediction accuracy, and disease type used in the ANN model were extracted and charted, then presented as a table along with narrative synthesis. Eighteen Studies were included in this scoping review, with 16 different types of musculoskeletal diseases. The accuracy of the ANN model predictions ranged from 0.542 to 0.947. ANN models were more accurate compared to traditional logistic regression models. This scoping review suggests that ANN can predict the prognosis of musculoskeletal diseases, which has the potential to be applied to different types of MSD.