Detection and prediction of osteoarthritis in knee and hand joints based on the X-ray image analysis

Algorithmic approaches in hand imaging for rheumatic musculoskeletal diseases: A systematic literature review

OBJECTIVE

This systematic literature review provides a comprehensive overview of the use of machine learning (ML) in hand imaging of rheumatic musculoskeletal diseases (RMDs). The review evaluates ML algorithms, imaging modalities, patient populations, validation methods, and areas for improvement.

METHODS

The review was conducted following PRISMA guidelines and registered with PROSPERO. Articles were retrieved from PubMed, EMBASE, and Scopus using relevant MeSH terms and keywords. The search, executed in October 2024, was conducted manually and with BiBot, an AI-based tool for literature reviews. Studies focusing on ML applications in osteoarthritis (OA), rheumatoid arthritis (RA), and psoriatic arthritis (PsA) were included.

RESULTS

From 400 initially identified studies, 32 met the inclusion criteria. RA was the most studied disease (88 %), followed by OA (22 %) and PsA (9 %). Convolutional neural networks (CNNs) were the most frequently used algorithms (50 %). Standard radiographs (59 %) were the predominant imaging modality, followed by MRI (16 %). Despite recommendations for ML studies, external validation was conducted in only 15 % of studies, and just 6 % of datasets were publicly available. Interpretability tools were employed in 28 % of studies to enhance clinical relevance.

CONCLUSION

ML has significant potential to improve diagnostics and disease management in hand imaging of RMDs. However, key challenges remain, including the need for increased external validation, broader disease coverage (OA and PsA), and improved data-sharing practices to enhance reproducibility and clinical adoption.

Bone Data Lake: A storage platform for bone texture analysis

Trabecular bone (TB) texture regions selected on hand and knee X-ray images can be used to detect and predict osteoarthritis (OA). However, the analysis has been impeded by increasing data volume and diversification of data formats. To address this problem, a novel storage platform, called Bone Data Lake (BDL) is proposed for the collection and retention of large numbers of images, TB texture regions and parameters, regardless of their structure, size and source. BDL consists of three components, i.e.: a raw data storage, a processed data storage, and a data reference system. The performance of the BDL was evaluated using 20,000 knee and hand X-ray images of various formats (DICOM, PNG, JPEG, BMP, and compressed TIFF) and sizes (from 0.3 to 66.7 MB). The images were uploaded into BDL and automatically converted into a standardized 8-bit grayscale uncompressed TIFF format. TB regions of interest were then selected on the standardized images, and a data catalog containing metadata information about the regions was constructed. Next, TB texture parameters were calculated for the regions using Variance Orientation Transform (VOT) and Augmented VOT (AVOT) methods and stored in XLSX files. The files were uploaded into BDL, and then transformed into CSV files and cataloged. Results showed that the BDL efficiently transforms images and catalogs bone regions and texture parameters. BDL can serve as the foundation of a reliable, secure and collaborative system for OA detection and prediction based on radiographs and TB texture.

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.

An intelligent IoMT enabled feature extraction method for early detection of knee arthritis

Osteoarthritis and rheumatoid are most common form of arthritis disorder, affecting millions of people worldwide. This article presents a computer aided detection system (CAD) for early knee osteoarthritis and rheumatoid detection using X‐ray images and machine learning classifiers. This work also proposed a novel feature extractor from X‐ray images of knee to assist in detection and classification, called explainable Renyi entropic segmentation with Internet of Things (IoT) framework. The proposed method later utilizes model agnostic algorithm using post hoc explainability for extracting relevant information from prediction of knee joint segmentation. CAD system is integrated with an IoT framework and can be used remotely to assist medical practitioners in treatments of knee arthritis. The presented results show commendable improvement over different existing feature extractors in combination with different classifiers. The best result of proposed extractor method was obtained when combined with random forest classifier having Euclidean hyperparameter that gave an accuracy of 95.23%, among all the evaluators. The obtained results show the effectiveness of proposed feature extractor model to determine relevant features from knee and describe the suitable knee disorders.

Applications and prospects of intra-articular drug delivery system in arthritis therapeutics

Arthritis is a kind of chronic disease that affects joints and muscles with the symptoms of joint pain, inflammation and limited movement of joints. Among various clinical therapies, drug therapy has been extensively applied because of its accessibility, safety and effectiveness. In recent years, the intra-articular injection has dramatic therapeutic effects in treating arthritis with high patient compliance and low side effects. In this review, we will introduce pathology of arthritis, along with the accessible treatment and diagnosis methods, then we will summarize major advances of current hopeful intra-articular delivery systems such as microspheres, hydrogels, nanoparticles and liposomes. At last, some safety assessments in the preclinical work and the main challenges for the further development of intra-articular treatment were also discussed.

An Automatic Knee Osteoarthritis Diagnosis Method Based on Deep Learning: Data from the Osteoarthritis Initiative

Osteoarthritis (OA) is the most common form of arthritis. According to the evidence presented on both sides of the knee bones, radiologists assess the severity of OA based on the Kellgren–Lawrence (KL) grading system. Recently, computer-aided methods are proposed to improve the efficiency of OA diagnosis. However, the human interventions required by previous semiautomatic segmentation methods limit the application on large-scale datasets. Moreover, well-known CNN architectures applied to the OA severity assessment do not explore the relations between different local regions. In this work, by integrating the object detection model, YOLO, with the visual transformer into the diagnosis procedure, we reduce human intervention and provide an end-to-end approach to automatic osteoarthritis diagnosis. Our approach correctly segments 95.57% of data at the expense of training on 200 annotated images on a large dataset that contains more than 4500 samples. Furthermore, our classification result improves the accuracy by 2.5% compared to the traditional CNN architectures.

Digital health, big data and smart technologies for the care of patients with systemic autoimmune diseases: Where do we stand?

The past decade has seen tremendous development in digital health, including in innovative new technologies such as Electronic Health Records, telemedicine, virtual visits, wearable technology and sophisticated analytical tools such as artificial intelligence (AI) and machine learning for the deep-integration of big data. In the field of rare connective tissue diseases (rCTDs), these opportunities include increased access to scarce and remote expertise, improved patient monitoring, increased participation and therapeutic adherence, better patient outcomes and patient empowerment. In this review, we discuss opportunities and key-barriers to improve application of digital health technologies in the field of autoimmune diseases. We also describe what could be the fully digital pathway of rCTD patients. Smart technologies can be used to provide real-world evidence about the natural history of rCTDs, to determine real-life drug utilization, advanced efficacy and safety data for rare diseases and highlight significant unmet needs. Yet, digitalization remains one of the most challenging issues faced by rCTD patients, their physicians and healthcare systems. Digital health technologies offer enormous potential to improve autoimmune rCTD care but this potential has so far been largely unrealized due to those significant obstacles. The need for robust assessments of the efficacy, affordability and scalability of AI in the context of digital health is crucial to improve the care of patients with rare autoimmune diseases.

Cissus quadrangularis (veldt grape) attenuates disease progression and anatomical changes in mono sodium iodoacetate (MIA)-induced knee osteoarthritis in the rat model

The Cissus quadrangularis (CQ) stem has interesting nutritional and pharmacological properties to promote the health of the skeletal system. It is a well-recognized plant in the conventional system of medicine in India for treating bone and joint-associated complications. This study focuses on identifying the active constituents from the stem and root extracts of CQ and validating its anti-osteoarthritic activity by the in vivo model. Notable levels of phenolics and flavonoids were found in the ethanol extracts of both CQ stem (CQSE) and root (CQRE), among other solvent fractions. UPLC-MS/MS analysis of these selective extracts resulted in different classes of active compounds from both positive and negative ionization modes. By analyzing their mass spectra and fragmentation pattern, 25 active compounds were identified. The CQSE and CQRE extracts, along with the standard drug (naproxen), were further tested in mono-sodium iodoacetate-induced experimental OA animals. The modulatory effects of the test extracts were assessed by haematology, synovial and cartilage marker profiling, radiology and histopathological analysis. The in vivo findings from the biochemical and physiological studies have led to the conclusion that the CQSE extract is a good choice for the management of OA. The results were substantially better than CQ root extract and naproxen drug-treated groups. Thus, CQS has bioactive constituents, which could facilitate recovery from joint tissue damage, cellular metabolism and associated risk factors attributable to dysfunctions in OA incidence and progression.

Deep learning for early detection of pathological changes in X-ray bone microstructures: case of osteoarthritis

Texture features are designed to quantitatively evaluate patterns of spatial distribution of image pixels for purposes of image analysis and interpretation. Unexplained variations in the texture patterns often lead to misinterpretation and undesirable consequences in medical image analysis. In this paper we explore the ability of machine learning (ML) methods to design a radiology test of Osteoarthritis (OA) at early stage when the number of patients’ cases is small. In our experiments we use high-resolution X-ray images of knees in patients which were identified with Kellgren–Lawrence scores progressing from 1. The existing ML methods have provided a limited diagnostic accuracy, whilst the proposed Group Method of Data Handling strategy of Deep Learning has significantly extended the diagnostic test. The comparative experiments demonstrate that the proposed framework using the Zernike-based texture features has significantly improved the diagnostic accuracy on average by 11%. This allows us to conclude that the designed model for early diagnostic of OA will provide more accurate radiology tests, although new study is required when a large number of patients’ cases will be available.

A novel approach for knee osteoarthritis using high molecular weight hyaluronic acid conjugated to plasma fibrinogen - interim findings of a double-blind clinical study

Objective

Osteoarthritis (OA) is a widespread degenerative joint disease leading to progressive loss of function and pain. Available treatments do not provide long-term relief or improvement. This study aimed to assess the safety and efficacy of a novel intra articular supplement, made of high molecular-weight hyaluronic acid (HA) uniquely conjugated to either purified (RegenoGel) or autologous plasma-derived fibrinogen (RegenoGel-OSP), as a long-term treatment for knee OA.

Methods

Sixty-seven consecutive participants (mean age 67.26 ± 7 years) with symptomatic OA were randomly assigned to receive intraarticular injections of either RegenoGel, RegenoGel-OSP or saline solution (placebo). The active treatment groups received a second, repeat injection of the corresponding treatment at the 3-month evaluation, at which time, the placebo group was divided into two subgroups, one receiving RegenoGel and the other receiving RegenoGel-OSP. The OA symptoms were assessed by VAS, WOMAC, and IKDC questionnaires at baseline and at 1, 3, 4, and 6 months following the first injection. OA-related quality of life was evaluated by the SF-12 survey.

Results

Our preliminary data suggests that both fibrin-HA formulations have positive effects on OA symptoms for all assessed parameters with the most prominent trend for reduction in OA-associated pain. Pooled data analysis of RegenoGel and RegenoGel-OSP shows significantly improved VAS scores compared to placebo at three months after the first injection, and sustained for another three months after the second injection. Both RegenoGel, RegenoGel-OSP had an excellent safety profile.

Conclusions

Interim analysis results indicate that RegenoGel and RegenoGel-OSP are safe and are potentially effective for at least six months in alleviating pain and symptoms of knee OA.

Spatially matching morphometric assessment of cartilage and subchondral bone in osteoarthritic human knee joint with micro-computed tomography

OBJECTIVE

The objective of this study was to develop a reproducible and semi-automatic method based on micro computed tomography (microCT) to analyze cartilage and bone morphology of human osteoarthritic knee joints in spatially matching regions of interest.

MATERIALS AND METHODS

Tibial plateaus from randomly selected patients with advanced osteoarthritis (OA) who underwent total knee arthroplasty surgery were microCT scanned once fresh and once after staining with Hexabrix. The articular surface was determined manually in the first scan. Total articular surface, defect surface and cartilage surface were computed by triangulation of the cartilage surface and the spatially corresponding subchondral bone regions were automatically generated and the standard cortical bone and trabecular bone morphometric indices were computed.

RESULTS

The method to identify cartilage surface and defects was successfully validated against photographic examinations. The microCT measurements of the cartilage defect were also verified by conventional histopathology using safranin O-stained sections. Cartilage thickness and volume was significantly lower for OA condyle compared with healthy condyle. Bone fraction, bone tissue mineral density, cortical density and trabecular thickness differed significantly depending on the level of cartilage damage.

CONCLUSION

This new microCT imaging workflow can be used for reproducible quantitative evaluation of articular cartilage damage and the associated changes in subchondral bone morphology in osteoarthritic joints with a relatively high throughput compared to manual contouring. This methodology can be applied to gain better understanding of the OA disease progress in large cohorts.

A decision support tool for early detection of knee OsteoArthritis using X-ray imaging and machine learning: Data from the OsteoArthritis Initiative

This paper presents a fully developed computer aided diagnosis (CAD) system for early knee OsteoArthritis (OA) detection using knee X-ray imaging and machine learning algorithms. The X-ray images are first preprocessed in the Fourier domain using a circular Fourier filter. Then, a novel normalization method based on predictive modeling using multivariate linear regression (MLR) is applied to the data in order to reduce the variability between OA and healthy subjects. At the feature selection/extraction stage, an independent component analysis (ICA) approach is used in order to reduce the dimensionality. Finally, Naive Bayes and random forest classifiers are used for the classification task. This novel image-based approach is applied on 1024 knee X-ray images from the public database OsteoArthritis Initiative (OAI). The results show that the proposed system has a good predictive classification rate for OA detection (82.98% for accuracy, 87.15% for sensitivity and up to 80.65% for specificity).

Computer-based automated analysis of X-ray and thermal imaging of knee region in evaluation of rheumatoid arthritis

The aim and objectives of the study are as follows: (1) to perform automated segmentation of knee X-ray images using fast greedy snake algorithm and feature extraction using gray level co-occurrence matrix method, (2) to implement automated segmentation of knee thermal image using RGB segmentation method and (3) to compare the features extracted from the segmented knee region of X-ray and thermal images in rheumatoid arthritis patients using a biochemical method as standard. In all, 30 rheumatoid arthritis patients and 30 age- and sex-matched healthy volunteers were included in the study. X-ray and thermography images of knee regions were acquired, and biochemical tests were carried out subsequently. The X-ray images were segmented using fast greedy snake algorithm, and feature extractions were performed using gray level co-occurrence matrix method. The thermal image was segmented using RGB-based segmentation method and statistical features were extracted. Statistical features extracted after segmentation from X-ray and thermal imaging of knee region were correlated with the standard biochemical parameters. The erythrocyte sedimentation rate shows statistically significant correlations (p < 0.01) with the X-ray parameters such as joint space width and % combined cortical thickness. The skin surface temperature measured from knee region of thermal imaging was highly correlated with erythrocyte sedimentation rate. Among all the extracted features namely mean, variance, energy, homogeneity and difference entropy depict statistically significant percentage differences between the rheumatoid arthritis and healthy subjects. From this study, it was observed that thermal infrared imaging technique serves as a potential tool in the evaluation of rheumatoid arthritis at an earlier stage compared to radiography. Hence, it was predicted that thermal imaging method has a competency in the diagnosis of rheumatoid arthritis by automated segmentation methods.