Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis

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.

Artificial intelligence in osteoarthritis detection: A systematic review and meta-analysis

OBJECTIVES

As an increasing number of studies apply artificial intelligence (AI) algorithms in osteoarthritis (OA) detection, we performed a systematic review and meta-analysis to pool the data on diagnostic performance metrics of AI, and to compare them with clinicians' performance.

MATERIALS AND METHODS

A search in PubMed and Scopus was performed to find studies published up to April 2022 that evaluated and/or validated an AI algorithm for the detection or classification of OA. We performed a meta-analysis to pool the data on the metrics of diagnostic performance. Subgroup analysis based on the involved joint and meta-regression based on multiple parameters were performed to find potential sources of heterogeneity. The risk of bias was assessed using Prediction Model Study Risk of Bias Assessment Tool reporting guidelines.

RESULTS

Of the 61 studies included, 27 studies with 91 contingency tables provided sufficient data to enter the meta-analysis. The pooled sensitivities for AI algorithms and clinicians on internal validation test sets were 88% (95% confidence interval [CI]: 86,91) and 80% (95% CI: 68,88) and pooled specificities were 81% (95% CI: 75,85) and 79% (95% CI: 80,85), respectively. At external validation, the pooled sensitivity and specificity for AI algorithms were 94% (95% CI: 90,97) and 91% (95% CI: 77,97), respectively.

CONCLUSION

Although the results of this meta-analysis should be interpreted with caution due to the potential pitfalls in the included studies, the promising role of AI as a diagnostic adjunct to radiologists is indisputable.

Retracted: Multicentre Study Using Machine Learning Methods in Clinical Diagnosis of Knee Osteoarthritis

[This retracts the article DOI: 10.1155/2021/1765404.].

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.

Identification for heavy metals exposure on osteoarthritis among aging people and Machine learning for prediction: A study based on NHANES 2011-2020

Objective

Heavy metals are present in many environmental pollutants, and have cumulative effects on the human body through water or food, which can lead to several diseases, including osteoarthritis (OA). In this research, we aimed to explore the association between heavy metals and OA.

Methods

We extracted 18 variables including age, gender, race, education level, marital status, smoking status, body mass index (BMI), physical activity, diabetes mellitus, hypertension, poverty level index (PLI), Lead (Pb), cadmium (Cd), mercury (Hg), selenium (Se), manganese (Mn), and OA status from National Health and Nutrition Examination Survey (NHANES) 2011-2020 datasets.

Results

In the baseline data, the t test and Chi-square test were conducted. For heavy metals, quartile description and limit of detection (LOD) were adopted. To analyze the association between heavy metals and OA among elderly subjects, multivariable logistic regression was conducted and subgroup logistic by gender was also carried out. Furthermore, to make predictions based on heavy metals for OA, we compared eight machine learning algorithms, and XGBoost (AUC of 0.8, accuracy value of 0.773, and kappa value of 0.358) was the best machine learning model for prediction. For interactive use, a shiny application was made (https://alanwu.shinyapps.io/NHANES-OA/).

Conclusion

The overall and gender subgroup logistic regressions all showed that Pb and Cd promoted the prevalence of OA while Mn could be a protective factor of OA prevalence among the elderly population of the United States. Furthermore, XGBoost model was trained for OA prediction.