Contemporary Machine Learning Approaches Towards Biomechanical Analysis in the Diagnosis and Prognosis Prediction of Knee Osteoarthritis: A Systematic Review
Bibliographic record
Abstract
Introduction: Knee Osteoarthritis (KOA) is the second most reported condition for persons 50 years and up; approximated by the continuous degradation of the knee, and eventually extending to the debilitation of biomechanical gait parameters. Inconsistencies with existing diagnostic methods mean that Machine Learning (ML) has been leveraged in creating gait-based predictive models in relation to KOA. The purpose of this study is to explore existing literature with camera and sensor-based methodologies, along with the employed algorithms in the diagnosis and prognosis prediction of KOA. Methods: Searches for literature were accomplished on Google Scholar and PUBMED databases using relevant keywords, within a time frame of 2010 - 2023. Information pertaining to the data collection method, algorithm used, and model performance was collected. Results: After the initial search of 1132 articles, the selection process yielded 22 articles for further review. Of the 22 articles, 10% focused on the prediction of patient outcomes and disease prognosis, while 90% focused on the initial diagnosis or severity prediction of KOA. 28% of the reviewed literature utilized sensor-based technology for biomechanical gait parameter collection, while the remainder utilized a more traditional camera-based approach. While evaluatory metrics varied between studies, of the studies with reported accuracy metrics (n=11), camera-based models had on average a higher accuracy compared to sensor-based algorithms, 92.05% compared to 67.96%, respectively. Discussion: Support Vector Machine (SVM) was found to be the most common algorithm used within the reviewed studies, and had the highest accuracy on average, possibly attributed to the ability of the algorithm to manage small yet high dimensional datasets. The difference in accuracy between camera-based and sensor-based approaches was determined to be statistically significant through application of a Mann-Whitney U Test. While sensors have a reduced quantity of features capable of being measured, it is a more applicable technology for clinical application, indicating an area for future development. Conclusion: Overall, literature concerning the binary classification of symptomatic KOA provided high accuracy, yet further validation to minimize overfitting is required. Furthermore, areas for prognosis prediction and multiclass classification of KOA severity remain as areas for further development.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.000 |
| Bibliometrics | 0.003 | 0.008 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.004 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".