Causal Discovery in Radiographic Markers of Knee Osteoarthritis and Prediction for Knee Osteoarthritis Severity With Attention-Long Short-Term Memory

Economic Evaluation of Total Knee Replacement Compared with Non-Surgical Management for Knee Osteoarthritis in India

OBJECTIVE

This study is an economic evaluation of total knee replacement (TKR) in comparison with non-surgical management in India.

METHODS

Cost-utility analysis and budget impact analysis (BIA) were conducted on individuals aged ≥ 50 years with osteoarthritis of the knee (OA knee) Kellgren-Lawrence grades 2 and 3 using a provider's perspective. Three scenarios were considered, varying the age at which TKR is administered while assuming a 20-year lifespan for the implant. A Markov model was used to determine incremental cost-effectiveness ratios (ICERs). Sensitivity analysis was conducted incorporating implant costs and other input parameters.

RESULTS

Net quality-adjusted life-years (QALYs) gained per OA knee treated with TKR were superior when performed at the age of 50, regardless of OA severity and across all scenarios. The lowest ICER was 36,107 Indian National Rupees (INR) (USD 482.9)/QALY gained, observed at 50 years, while the highest was INR 61,363 (USD 820.72)/QALY gained at 70 years for grade-2 severity. Sensitivity analysis revealed that the ICER was most sensitive to the cost of non-surgical management, health utility values gained in an improved state, and the cost of TKR across scenarios. For the BIA in Scenario 1, with 40% coverage for TKR, costs reach INR 5013 crores (cr) (USD 670,477,060) in 2023 and INR 8444 cr (USD 1,024,628,736) in 2028 (1% of government budgets). In Scenario 2 (full coverage), costs are INR 12,532 cr (USD 1,520,683,008) (2.7%) in 2023, declining to 2.4% in 2028. In Scenario 3, covering 40% under the National Health Mission (NHM), costs vary from 17% in 2023 to 25% in 2028.

CONCLUSION

This study concludes that TKR is a cost-effective treatment option compared with non-surgical management for OA knee in India, irrespective of age, implant types, and severity.

Predicting joint space changes in knee osteoarthritis over 6 years: a combined model of TransUNet and XGBoost

Background

The progression of knee osteoarthritis is mainly characterized by the reduction in joint space width (JSW). The goal of this study was to build a knee joint space segmentation model through deep learning (DL) methods and develop a model for automatically measuring JSW. Furthermore, we predicted JSW changes in the sixth year based on regression models.

Methods

The data for this study was sourced from the Osteoarthritis Initiative database. We filtered knee X-ray images from 1,947 participants and tested six neural networks for segmentation to build an automatic JSW measurement model. Subsequently, we combined the clinical data with the JSW measurement results to predict the sixth-year knee JSW using six different regression models.

Results

The segmentation results showed that TransUNet performed the best, with an overall Dice coefficient of 0.889. The intraclass correlation coefficient (ICC) between manually measured and TransUNet's automatically measured JSW reached 0.927 (P<0.01). Among the regression models, eXtreme Gradient Boosting (XGBoost) demonstrated the best predictive performance, with a mean absolute error (MAE) of 0.48 and an ICC of 0.887 (P<0.01). To better align with clinical practice, we reduced the prediction model to utilize only 2 years of JSW images. The results showed that using the 0- and 12-month X-ray images still achieved high accuracy, with an MAE of 0.585 (P<0.05) and an ICC of 0.805 (P<0.01).

Conclusions

We developed a novel JSW measurement model that significantly improves accuracy compared to previous methods and identified the best prediction model by combining TransUNet and XGBoost. Additionally, in our built model, predicting the 72-month JSW using only 2 years of knee X-ray images and several clinical features achieved high accuracy.

Understanding the role of machine learning in predicting progression of osteoarthritis

Aims

Machine learning (ML), a branch of artificial intelligence that uses algorithms to learn from data and make predictions, offers a pathway towards more personalized and tailored surgical treatments. This approach is particularly relevant to prevalent joint diseases such as osteoarthritis (OA). In contrast to end-stage disease, where joint arthroplasty provides excellent results, early stages of OA currently lack effective therapies to halt or reverse progression. Accurate prediction of OA progression is crucial if timely interventions are to be developed, to enhance patient care and optimize the design of clinical trials.

Methods

A systematic review was conducted in accordance with PRISMA guidelines. We searched MEDLINE and Embase on 5 May 2024 for studies utilizing ML to predict OA progression. Titles and abstracts were independently screened, followed by full-text reviews for studies that met the eligibility criteria. Key information was extracted and synthesized for analysis, including types of data (such as clinical, radiological, or biochemical), definitions of OA progression, ML algorithms, validation methods, and outcome measures.

Results

Out of 1,160 studies initially identified, 39 were included. Most studies (85%) were published between 2020 and 2024, with 82% using publicly available datasets, primarily the Osteoarthritis Initiative. ML methods were predominantly supervised, with significant variability in the definitions of OA progression: most studies focused on structural changes (59%), while fewer addressed pain progression or both. Deep learning was used in 44% of studies, while automated ML was used in 5%. There was a lack of standardization in evaluation metrics and limited external validation. Interpretability was explored in 54% of studies, primarily using SHapley Additive exPlanations.

Conclusion

Our systematic review demonstrates the feasibility of ML models in predicting OA progression, but also uncovers critical limitations that currently restrict their clinical applicability. Future priorities should include diversifying data sources, standardizing outcome measures, enforcing rigorous validation, and integrating more sophisticated algorithms. This paradigm shift from predictive modelling to actionable clinical tools has the potential to transform patient care and disease management in orthopaedic practice.

Deep learning to combat knee osteoarthritis and severity assessment by using CNN-based classification

BACKGROUND

In today's digital age, various diseases drastically reduce people's quality of life. Arthritis is one amongst the most common and debilitating maladies. Osteoarthritis affects several joints, including the hands, knees, spine, and hips. This study focuses on the medical disorder underlying Knee Osteoarthritis (KOA) which severely impairs people's quality of life. KOA is characterised by restricted mobility, stiffness, and terrible pain and can be caused by a range of factors such as ageing, obesity, and traumas. This degenerative disorder leads to progressive wear and tear of the knee joint.

METHODS

To combat arthritis in the kneecap, this study employs a 12-layer Convolutional Neural Network (CNN) to reach deep learning capabilities. A collection of data from the Osteoarthritis Initiative (OAI) is used to classify KOA. Through the use of medical image processing; the study ascertains whether an individual has this ailment. A sophisticated CNN architecture created especially for binary classification and KOA severity utilising deep learning algorithms is the main component of this work.

RESULTS

The cross-entropy loss function is an important component of the model's laborious design that classifies data into two groups. The remaining section uses the Kellgren-Lawrence (KL) grade to classify the disease's severity. In the binary classification, the proposed algorithm outperforms previous methods with an accuracy rate of 92.3%, and in the multiclassification, its accuracy rate is 78.4% which is superior to the previous findings.

CONCLUSION

Looking ahead, the research broadens the scope of this work by gathering information from various sources and using these methods on a wider range of datasets and situations. The potential for major advancements in the field of osteoarthritis detection and classification is highlighted by this forward-looking approach. Furthermore, this method reduces the intervention of medical practitioners and ultimately results in accurate diagnosis.

CLINICAL TRIAL NUMBER

Not applicable.

ABR-Attention: An Attention-Based Model for Precisely Localizing Auditory Brainstem Response

Auditory Brainstem Response (ABR) is an evoked potential in the brainstem's neural centers in response to sound stimuli. Clinically, characteristic waves, especially Wave V latency, extracted from ABR can objectively indicate auditory loss and diagnose diseases. Several methods have been developed for the extraction of characteristic waves. To ensure the effectiveness of the method, most of the methods are time-consuming and rely on the heavy workloads of clinicians. To reduce the workload of clinicians, automated extraction methods have been developed. However, the above methods also have limitations. This study introduces a novel deep learning network for automatic extraction of Wave V latency, named ABR-Attention. ABR-Attention model includes a self-attention module, first and second-derivative attention module, and regressor module. Experiments are conducted on the accuracy with 10-fold cross-validation, the effects on different sound pressure levels (SPLs), the effects of different error scales and the effects of ablation. ABR-Attention shows efficacy in extracting Wave V latency of ABR, with an overall accuracy of 96.76 ± 0.41 % and an error scale of 0.1ms, and provides a new solution for objective localization of ABR characteristic waves.

Adversarial Evolving Neural Network for Longitudinal Knee Osteoarthritis Prediction

Knee osteoarthritis (KOA) as a disabling joint disease has doubled in prevalence since the mid-20th century. Early diagnosis for the longitudinal KOA grades has been increasingly important for effective monitoring and intervention. Although recent studies have achieved promising performance for baseline KOA grading, longitudinal KOA grading has been seldom studied and the KOA domain knowledge has not been well explored yet. In this paper, a novel deep learning architecture, namely adversarial evolving neural network (A-ENN), is proposed for longitudinal grading of KOA severity. As the disease progresses from mild to severe level, ENN involves the progression patterns for accurately characterizing the disease by comparing an input image it to the template images of different KL grades using convolution and deconvolution computations. In addition, an adversarial training scheme with a discriminator is developed to obtain the evolution traces. Thus, the evolution traces as fine-grained domain knowledge are further fused with the general convolutional image representations for longitudinal grading. Note that ENN can be applied to other learning tasks together with existing deep architectures, in which the responses characterize progressive representations. Comprehensive experiments on the Osteoarthritis Initiative (OAI) dataset were conducted to evaluate the proposed method. An overall accuracy was achieved as 62.7%, with the baseline, 12-month, 24-month, 36-month, and 48-month accuracy as 64.6%, 63.9%, 63.2%, 61.8% and 60.2%, respectively.

Externally validated models for first diagnosis and risk of progression of knee osteoarthritis

OBJECTIVE

We develop and externally validate two models for use with radiological knee osteoarthritis. They consist of a diagnostic model for KOA and a prognostic model of time to onset of KOA. Model development and optimisation used data from the Osteoarthritis initiative (OAI) and external validation for both models was by application to data from the Multicenter Osteoarthritis Study (MOST).

MATERIALS AND METHODS

The diagnostic model at first presentation comprises subjects in the OAI with and without KOA (n = 2006), modelling with multivariate logistic regression. The prognostic sample involves 5-year follow-up of subjects presenting without clinical KOA (n = 1155), with modelling with Cox regression. In both instances the models used training data sets of n = 1353 and 1002 subjects and optimisation used test data sets of n = 1354 and 1003. The external validation data sets for the diagnostic and prognostic models comprised n = 2006 and n = 1155 subjects respectively.

RESULTS

The classification performance of the diagnostic model on the test data has an AUC of 0.748 (0.721-0.774) and 0.670 (0.631-0.708) in external validation. The survival model has concordance scores for the OAI test set of 0.74 (0.7325-0.7439) and in external validation 0.72 (0.7190-0.7373). The survival approach stratified the population into two risk cohorts. The separation between the cohorts remains when the model is applied to the validation data.

DISCUSSION

The models produced are interpretable with app interfaces that implement nomograms. The apps may be used for stratification and for patient education over the impact of modifiable risk factors. The externally validated results, by application to data from a substantial prospective observational study, show the robustness of models for likelihood of presenting with KOA at an initial assessment based on risk factors identified by the OAI protocol and stratification of risk for developing KOA in the next five years.

CONCLUSION

Modelling clinical KOA from OAI data validates well for the MOST data set. Both risk models identified key factors for differentiation of the target population from commonly available variables. With this analysis there is potential to improve clinical management of patients.

Biochemical, clinical, demographic and imaging biomarkers for disease progression in knee osteoarthritis

Aim: To identify prognostic biomarker(s) for knee osteoarthritis (OA) in the Osteoarthritis Initiative (OAI) cohort. Methods: Multilevel regression was used to determine the association between baseline biomarkers and change in biomarkers from baseline to 24 months with clinical and radiographic OA progression over 48 months of follow-up. Results: Higher values of baseline urinary CTXII were consistently associated with an increased risk of OA disease progression outcomes: Kellgren & Lawrence grade (odds ratio [OR]: 1.15, 95% CI: 1.03-1.28); medial joint space narrowing (OR: 1.06, 95% CI: 1.02-1.10); lateral osteophytes (OR: 1.05, 95% CI: 1.01-1.10); joint space width (regression coefficient: -0.005, 95% CI: -0.008-0.001); and Western Ontario and McMaster Universities Arthritis Index pain scores (OR: 1.02, 95% CI: 1.01-1.04). Changes in serum PIIANP and serum COMP over 24 months were associated with clinical disease progression. Conclusion: Urinary CTXII showed stronger associations with radiographic OA and appears to be a reliable prognostic marker, while changes in other biomarkers were found in early symptomatic OA, supporting the phasic nature of OA.

Identifying Robust Risk Factors for Knee Osteoarthritis Progression: An Evolutionary Machine Learning Approach

Knee osteoarthritis (KOA) is a multifactorial disease which is responsible for more than 80% of the osteoarthritis disease's total burden. KOA is heterogeneous in terms of rates of progression with several different phenotypes and a large number of risk factors, which often interact with each other. A number of modifiable and non-modifiable systemic and mechanical parameters along with comorbidities as well as pain-related factors contribute to the development of KOA. Although models exist to predict the onset of the disease or discriminate between asymptotic and OA patients, there are just a few studies in the recent literature that focused on the identification of risk factors associated with KOA progression. This paper contributes to the identification of risk factors for KOA progression via a robust feature selection (FS) methodology that overcomes two crucial challenges: (i) the observed high dimensionality and heterogeneity of the available data that are obtained from the Osteoarthritis Initiative (OAI) database and (ii) a severe class imbalance problem posed by the fact that the KOA progressors class is significantly smaller than the non-progressors' class. The proposed feature selection methodology relies on a combination of evolutionary algorithms and machine learning (ML) models, leading to the selection of a relatively small feature subset of 35 risk factors that generalizes well on the whole dataset (mean accuracy of 71.25%). We investigated the effectiveness of the proposed approach in a comparative analysis with well-known FS techniques with respect to metrics related to both prediction accuracy and generalization capability. The impact of the selected risk factors on the prediction output was further investigated using SHapley Additive exPlanations (SHAP). The proposed FS methodology may contribute to the development of new, efficient risk stratification strategies and identification of risk phenotypes of each KOA patient to enable appropriate interventions.

Prediction of Joint Space Narrowing Progression in Knee Osteoarthritis Patients

Osteoarthritis is a joint disease that commonly occurs in the knee (KOA). The continuous increase in medical data regarding KOA has triggered researchers to incorporate artificial intelligence analytics for KOA prognosis or treatment. In this study, two approaches are presented to predict the progression of knee joint space narrowing (JSN) in each knee and in both knees combined. A machine learning approach is proposed with the use of multidisciplinary data from the osteoarthritis initiative database. The proposed methodology employs: (i) A clustering process to identify groups of people with progressing and non-progressing JSN; (ii) a robust feature selection (FS) process consisting of filter, wrapper, and embedded techniques that identifies the most informative risk factors; (iii) a decision making process based on the evaluation and comparison of various classification algorithms towards the selection and development of the final predictive model for JSN; and (iv) post-hoc interpretation of the features' impact on the best performing model. The results showed that bounding the JSN progression of both knees can result to more robust prediction models with a higher accuracy (83.3%) and with fewer risk factors (29) compared to the right knee (77.7%, 88 risk factors) and the left knee (78.3%, 164 risk factors), separately.