Subchondral bone length in knee osteoarthritis: A deep learning derived imaging measure and its association with radiographic and clinical outcomes

Survival analysis on subchondral bone length for total knee replacement

OBJECTIVE

Use subchondral bone length (SBL), a new MRI-derived measure that reflects the extent of cartilage loss and bone flattening, to predict the risk of progression to total knee replacement (TKR).

METHODS

We employed baseline MRI data from the Osteoarthritis Initiative (OAI), focusing on 760 men and 1214 women with bone marrow lesions (BMLs) and joint space narrowing (JSN) scores, to predict the progression to TKR. To minimize bias from analyzing both knees of a participant, only the knee with a higher Kellgren-Lawrence (KL) grade was considered, given its greater potential need for TKR. We utilized the Kaplan-Meier survival curves and Cox proportional hazards models, incorporating raw and normalized values of SBL, JSN, and BML as predictors. The study included subgroup analyses for different demographics and clinical characteristics, using models for raw and normalized SBL (merged, femoral, tibial), BML (merged, femoral, tibial), and JSN (medial and lateral compartments). Model performance was evaluated using the time-dependent area under the curve (AUC), Brier score, and Concordance index to gauge accuracy, calibration, and discriminatory power. Knee joint and region-level analyses were conducted to determine the effectiveness of SBL, JSN, and BML in predicting TKR risk.

RESULTS

The SBL model, incorporating data from both the femur and tibia, demonstrated a predictive capacity for TKR that closely matched the performance of the BML score and the JSN grade. The Concordance index of the SBL model was 0.764, closely mirroring the BML's 0.759 and slightly below JSN's 0.788. The Brier score for the SBL model stood at 0.069, showing comparability with BML's 0.073 and a minor difference from JSN's 0.067. Regarding the AUC, the SBL model achieved 0.803, nearly identical to BML's 0.802 and slightly lower than JSN's 0.827.

CONCLUSION

SBL's capacity to predict the risk of progression to TKR highlights its potential as an effective imaging biomarker for knee osteoarthritis.

A radiograph-based deep learning model improves radiologists' performance for classification of histological types of primary bone tumors: A multicenter study

PURPOSE

To develop a deep learning (DL) model for classifying histological types of primary bone tumors (PBTs) using radiographs and evaluate its clinical utility in assisting radiologists.

METHODS

This retrospective study included 878 patients with pathologically confirmed PBTs from two centers (638, 77, 80, and 83 for the training, validation, internal test, and external test sets, respectively). We classified PBTs into five categories by histological types: chondrogenic tumors, osteogenic tumors, osteoclastic giant cell-rich tumors, other mesenchymal tumors of bone, or other histological types of PBTs. A DL model combining radiographs and clinical features based on the EfficientNet-B3 was developed for five-category classification. The area under the receiver operating characteristic curve (AUC), accuracy, sensitivity, and specificity were calculated to evaluate model performance. The clinical utility of the model was evaluated in an observer study with four radiologists.

RESULTS

The combined model achieved a macro average AUC of 0.904/0.873, with an accuracy of 67.5 %/68.7 %, a macro average sensitivity of 66.9 %/57.2 %, and a macro average specificity of 92.1 %/91.6 % on the internal/external test set, respectively. Model-assisted analysis improved accuracy, interpretation time, and confidence for junior (50.6 % vs. 72.3 %, 53.07[s] vs. 18.55[s] and 3.10 vs. 3.73 on a 5-point Likert scale [P < 0.05 for each], respectively) and senior radiologists (68.7 % vs. 75.3 %, 32.50[s] vs. 21.42[s] and 4.19 vs. 4.37 [P < 0.05 for each], respectively).

CONCLUSION

The combined DL model effectively classified histological types of PBTs and assisted radiologists in achieving better classification results than their independent visual assessment.

Subchondral bone expansion in advanced knee osteoarthritis: Relation with radiographic severity and role in surgical decision-making

Background

Joint space width (JSW) is a traditional imaging marker for knee osteoarthritis (OA) severity, but it lacks sensitivity in advanced cases. We propose tibial subchondral bone area (TSBA), a new CT imaging marker to explore its relationship with OA radiographic severity, and to test its performance for classifying surgical decisions between unicompartmental knee arthroplasty (UKA) and total knee arthroplasty (TKA) compared to JSW.

Methods

We collected clinical, radiograph, and CT data from 182 patients who underwent primary knee arthroplasty (73 UKA, 109 TKA). The radiographic severity was scored using Kellgren-Lawrence (KL) grading system. TSBA and JSW were extracted from 3D CT-reconstruction model. We used independent t-test to investigate the relationship between TSBA and KL grade, and binary logistic regression to identify factors associated with TKA risk. The accuracy of TSBA, JSW and established classification model in differentiating between UKA and TKA was assessed using AUC.

Results

All parameters exhibited inter- and intra-class coefficients greater than 0.966. Patients with KL grade 4 had significantly larger TSBA than those with KL grade 3. TSBA (0.708 of AUC) was superior to minimal/average JSW (0.547/0.554 of AUC) associated with the risk of receiving TKA. Medial TSBA, together with gender and Knee Society Knee Score, emerged as independent classification factors in multivariate analysis. The overall AUC of composite model for surgical decision-making was 0.822.

Conclusion

Tibial subchondral bone area is an independent imaging marker for radiographic severity, and is superior to JSW for surgical decision-making between UKA and TKA in advanced OA patients.

Artificial intelligence to analyze magnetic resonance imaging in rheumatology

Rheumatic disorders present a global health challenge, marked by inflammation and damage to joints, bones, and connective tissues. Accurate, timely diagnosis and appropriate management are crucial for favorable patient outcomes. Magnetic resonance imaging (MRI) has become indispensable in rheumatology, but interpretation remains laborious and variable. Artificial intelligence (AI), including machine learning (ML) and deep learning (DL), offers a means to improve and advance MRI analysis. This review examines current AI applications in rheumatology MRI analysis, addressing diagnostic support, disease classification, activity assessment, and progression monitoring. AI demonstrates promise, with high sensitivity, specificity, and accuracy, achieving or surpassing expert performance. The review also discusses clinical implementation challenges and future research directions to enhance rheumatic disease diagnosis and management.

Deep learning in rheumatological image interpretation

Artificial intelligence techniques, specifically deep learning, have already affected daily life in a wide range of areas. Likewise, initial applications have been explored in rheumatology. Deep learning might not easily surpass the accuracy of classic techniques when performing classification or regression on low-dimensional numerical data. With images as input, however, deep learning has become so successful that it has already outperformed the majority of conventional image-processing techniques developed during the past 50 years. As with any new imaging technology, rheumatologists and radiologists need to consider adapting their arsenal of diagnostic, prognostic and monitoring tools, and even their clinical role and collaborations. This adaptation requires a basic understanding of the technical background of deep learning, to efficiently utilize its benefits but also to recognize its drawbacks and pitfalls, as blindly relying on deep learning might be at odds with its capabilities. To facilitate such an understanding, it is necessary to provide an overview of deep-learning techniques for automatic image analysis in detecting, quantifying, predicting and monitoring rheumatic diseases, and of currently published deep-learning applications in radiological imaging for rheumatology, with critical assessment of possible limitations, errors and confounders, and conceivable consequences for rheumatologists and radiologists in clinical practice.

Single Injection AAV2-FGF18 Gene Therapy Reduces Cartilage Loss and Subchondral Bone Damage in a Mechanically Induced Model of Osteoarthritis

BACKGROUND

Osteoarthritis (OA) is a highly debilitating, degenerative pathology of cartilaginous joints affecting over 500 million people worldwide. The global economic burden of OA is estimated at $260-519 billion and growing, driven by aging global population and increasing rates of obesity. To date, only the multi-injection chondroanabolic treatment regimen of Fibroblast Growth Factor 18 (FGF18) has demonstrated clinically meaningful disease-modifying efficacy in placebo-controlled human trials. Our work focuses on the development of a novel single injection disease-modifying gene therapy, based on FGF18's chondroanabolic activity.

METHODS

OA was induced in Sprague-Dawley rats using destabilization of the medial meniscus (DMM) (3 weeks), followed by intra-articular treatment with 3 dose levels of AAV2-FGF18, rh- FGF18 protein, and PBS. Durability, redosability, and biodistribution were measured by quantifying nLuc reporter bioluminescence. Transcriptomic analysis was performed by RNA-seq on cultured human chondrocytes and rat knee joints. Morphological analysis was performed on knee joints stained with Safranin O/Fast Green and anti-PRG antibody.

RESULTS

Dose-dependent reductions in cartilage defect size were observed in the AAV2-FGF18- treated joints relative to the vehicle control. Total defect width was reduced by up to 76% and cartilage thickness in the thinnest zone was increased by up to 106%. Morphologically, the vehicle- treated joints exhibited pronounced degeneration, ranging from severe cartilage erosion and bone void formation, to subchondral bone remodeling and near-complete subchondral bone collapse. In contrast, AAV2-FGF18-treated joints appeared more anatomically normal, with only regional glycosaminoglycan loss and marginal cartilage erosion. While effective at reducing cartilage lesions, treatment with rhFGF18 injections resulted in significant joint swelling (19% increase in diameter), as well as a decrease in PRG4 staining uniformity and intensity. In contrast to early-timepoint in vitro RNA-seq analysis, which showed a high degree of concordance between protein- and gene therapy-treated chondrocytes, in vivo transcriptomic analysis, revealed few gene expression changes following protein treatment. On the other hand, the gene therapy treatment exhibited a high degree of durability and localization over the study period, upregulating several chondroanabolic genes while downregulating OA- and fibrocartilage-associated markers.

CONCLUSION

FGF18 gene therapy treatment of OA joints can provide benefits to both cartilage and subchondral bone, with a high degree of localization and durability.

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).

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.

Magnetic resonance imaging assessments for knee segmentation and their use in combination with machine/deep learning as predictors of early osteoarthritis diagnosis and prognosis

Knee osteoarthritis (OA) is a prevalent and disabling disease that can develop over decades. This disease is heterogeneous and involves structural changes in the whole joint, encompassing multiple tissue types. Detecting OA before the onset of irreversible changes is crucial for early management, and this could be achieved by allowing knee tissue visualization and quantifying their changes over time. Although some imaging modalities are available for knee structure assessment, magnetic resonance imaging (MRI) is preferred. This narrative review looks at existing literature, first on MRI-developed approaches for evaluating knee articular tissues, and second on prediction using machine/deep-learning-based methodologies and MRI as input or outcome for early OA diagnosis and prognosis. A substantial number of MRI methodologies have been developed to assess several knee tissues in a semi-quantitative and quantitative fashion using manual, semi-automated and fully automated systems. This dynamic field has grown substantially since the advent of machine/deep learning. Another active area is predictive modelling using machine/deep-learning methodologies enabling robust early OA diagnosis/prognosis. Moreover, incorporating MRI markers as input/outcome in such predictive models is important for a more accurate OA structural diagnosis/prognosis. The main limitation of their usage is the ability to move them in rheumatology practice. In conclusion, MRI knee tissue determination and quantification provide early indicators for individuals at high risk of developing this disease or for patient prognosis. Such assessment of knee tissues, combined with the development of models/tools from machine/deep learning using, in addition to other parameters, MRI markers for early diagnosis/prognosis, will maximize opportunities for individualized risk assessment for use in clinical practice permitting precision medicine. Future efforts should be made to integrate such prediction models into open access, allowing early disease management to prevent or delay the OA outcome.

The application of machine learning in early diagnosis of osteoarthritis: a narrative review

Osteoarthritis (OA) is the commonest musculoskeletal disease worldwide, with an increasing prevalence due to aging. It causes joint pain and disability, decreased quality of life, and a huge burden on healthcare services for society. However, the current main diagnostic methods are not suitable for early diagnosing patients of OA. The use of machine learning (ML) in OA diagnosis has increased dramatically in the past few years. Hence, in this review article, we describe the research progress in the application of ML in the early diagnosis of OA, discuss the current trends and limitations of ML approaches, and propose future research priorities to apply the tools in the field of OA. Accurate ML-based predictive models with imaging techniques that are sensitive to early changes in OA ahead of the emergence of clinical features are expected to address the current dilemma. The diagnostic ability of the fusion model that combines multidimensional information makes patient-specific early diagnosis and prognosis estimation of OA possible in the future.

A More Posterior Tibial Tubercle (Decreased Sagittal Tibial Tubercle-Trochlear Groove Distance) Is Significantly Associated With Patellofemoral Joint Degenerative Cartilage Change: A Deep Learning Analysis

PURPOSE

To perform patellofemoral joint (PFJ) geometric measurements on knee magnetic resonance imaging scans and determine their relations with chondral lesions in a multicenter cohort using deep learning.

METHODS

The sagittal tibial tubercle-trochlear groove (sTTTG) distance, tibial tubercle-trochlear groove distance, trochlear sulcus angle, trochlear depth, Caton-Deschamps Index (CDI), and flexion angle were measured by use of deep learning-generated segmentations on a subset of the Osteoarthritis Initiative study with radiologist-graded PFJ cartilage grades (n = 2,461). Kruskal-Wallis H tests were performed to compare differences in PFJ morphology between subjects without PFJ osteoarthritis (OA) and those with PFJ OA. PFJ morphology was correlated with secondary outcomes of mean patellar cartilage thickness and mean patellar cartilage T2 relaxation time using linear regression models controlling for age, sex, and body mass index.

RESULTS

A total of 1,626 knees did not have PFJ OA, whereas 835 knees had PFJ OA. Knees without PFJ OA had an increased (anterior) sTTTG distance (mean ± standard deviation, 11.1 ± 12.8 mm) compared with knees with PFJ OA (8.4 ± 12.7 mm) (P < .001), indicating a more posterior tibial tubercle in subjects with PFJ OA. Knees without PFJ OA had a decreased sulcus angle (127.4° ± 7.1° vs 128.0° ± 8.4°, P = .01) and increased trochlear depth (9.1 ± 1.7 mm vs 9.0 ± 2.0 mm, P = .03) compared with knees with PFJ OA. Decreased patellar cartilage thickness was associated with decreased trochlear depth (β = 0.12, P = .002) and increased CDI (β = -0.07, P < .001). Increased patellar cartilage T2 relaxation time was correlated with decreased sTTTG distance (β = -0.08, P = .01), decreased sulcus angle (β = -0.12, P = .04), and decreased CDI (β = -0.12, P < .001).

CONCLUSIONS

PFJ OA, patellar cartilage thickness, and patellar cartilage T2 relaxation time were shown to be associated with the underlying geometries within the PFJ. This large longitudinal study highlights that a decreased sTTTG distance (i.e., a more posterior tibial tubercle) is significantly associated with PFJ degenerative cartilage change.

LEVEL OF EVIDENCE

Level III, retrospective comparative prognostic trial.

Automatic assessment of knee osteoarthritis severity in portable devices based on deep learning

BACKGROUND

For knee osteoarthritis, the commonly used radiology severity criteria Kellgren-Lawrence lead to variability among surgeons. Most existing diagnosis models require preprocessed radiographs and specific equipment.

METHODS

All enrolled patients diagnosed with KOA who met the criteria were obtained from **** Hospital. This study included 2579 images shot from posterior-anterior X-rays of 2,378 patients. We used RefineDet to train and validate this deep learning-based diagnostic model. After developing the model, 823 images of 697 patients were enrolled as the test set. The whole test set was assessed by up to 5 surgeons and this diagnostic model. To evaluate the model's performance we compared the results of the model with the KOA severity diagnoses of surgeons based on K-L scales.

RESULTS

Compared to the diagnoses of surgeons, the model achieved an overall accuracy of 0.977. Its sensitivity (recall) for K-L 0 to 4 was 1.0, 0.972, 0.979, 0.983 and 0.989, respectively; for these diagnoses, the specificity of this model was 0.992, 0.997, 0.994, 0.991 and 0.995. The precision and F1-score were 0.5 and 0.667 for K-L 0, 0.914 and 0.930 for K-L 1, 0.978 and 0.971 for K-L 2, 0.981 and 0.974 for K-L 3, and 0.988 and 0.985 for K-L 4, respectively. All K-L scales perform AUC > 0.90. The quadratic weighted Kappa coefficient between the diagnostic model and surgeons was 0.815 (P < 0.01, 95% CI 0.727-0.903). The performance of the model is comparable to the clinical diagnosis of KOA. This model improved the efficiency and avoided cumbersome image preprocessing.

CONCLUSION

The deep learning-based diagnostic model can be used to assess the severity of KOA in portable devices according to the Kellgren-Lawrence scale. On the premise of improving diagnostic efficiency, the results are highly reliable and reproducible.

A Machine Learning Model to Predict Knee Osteoarthritis Cartilage Volume Changes over Time Using Baseline Bone Curvature

The hallmark of osteoarthritis (OA), the most prevalent musculoskeletal disease, is the loss of cartilage. By using machine learning (ML), we aimed to assess if baseline knee bone curvature (BC) could predict cartilage volume loss (CVL) at one year, and to develop a gender-based model. BC and cartilage volume were assessed on 1246 participants using magnetic resonance imaging. Variables included age, body mass index, and baseline values of eight BC regions. The outcome consisted of CVL at one year in 12 regions. Five ML methods were evaluated. Validation demonstrated very good accuracy for both genders (R ≥ 0.78), except the medial tibial plateau for the woman. In conclusion, we demonstrated, for the first time, that knee CVL at one year could be predicted using five baseline BC region values. This would benefit patients at risk of structural progressive knee OA.

Discovering Knee Osteoarthritis Imaging Features for Diagnosis and Prognosis: Review of Manual Imaging Grading and Machine Learning Approaches

Knee osteoarthritis (OA) is a deliberating joint disorder characterized by cartilage loss that can be captured by imaging modalities and translated into imaging features. Observing imaging features is a well-known objective assessment for knee OA disorder. However, the variety of imaging features is rarely discussed. This study reviews knee OA imaging features with respect to different imaging modalities for traditional OA diagnosis and updates recent image-based machine learning approaches for knee OA diagnosis and prognosis. Although most studies recognized X-ray as standard imaging option for knee OA diagnosis, the imaging features are limited to bony changes and less sensitive to short-term OA changes. Researchers have recommended the usage of MRI to study the hidden OA-related radiomic features in soft tissues and bony structures. Furthermore, ultrasound imaging features should be explored to make it more feasible for point-of-care diagnosis. Traditional knee OA diagnosis mainly relies on manual interpretation of medical images based on the Kellgren-Lawrence (KL) grading scheme, but this approach is consistently prone to human resource and time constraints and less effective for OA prevention. Recent studies revealed the capability of machine learning approaches in automating knee OA diagnosis and prognosis, through three major tasks: knee joint localization (detection and segmentation), classification of OA severity, and prediction of disease progression. AI-aided diagnostic models improved the quality of knee OA diagnosis significantly in terms of time taken, reproducibility, and accuracy. Prognostic ability was demonstrated by several prediction models in terms of estimating possible OA onset, OA deterioration, progressive pain, progressive structural change, progressive structural change with pain, and time to total knee replacement (TKR) incidence. Despite research gaps, machine learning techniques still manifest huge potential to work on demanding tasks such as early knee OA detection and estimation of future disease events, as well as fundamental tasks such as discovering the new imaging features and establishment of novel OA status measure. Continuous machine learning model enhancement may favour the discovery of new OA treatment in future.