Artificial Intelligence for Clinical Gait Diagnostics of Knee Osteoarthritis: An Evidence - based Review and Analysis
Bibliographic record
Abstract
Background Knee osteoarthritis (OA) remains a leading aetiology of disability worldwide. With recent advances in gait analysis, clinical assessment of such a knee-related condition has been improved. Although motion capture (mocap) technology is deemed the gold standard for gait analysis, it heavily relies on adequate data processing to yield clinically significant results. Moreover, gait data is non-linear and high-dimensional. Due to missing data involved in a mocap session and typical statistical assumptions, conventional data processing methods are unable to reveal the intrinsic patterns to predict gait abnormalities. Research question Albeit studies have demonstrated the potential of Artificial Intelligence (AI) algorithms to address these limitations, these algorithms have not gained wide acceptance amongst biomechanists. The most common AI algorithms used in gait analysis are based on machine learning (ML) and artificial neural networks (ANN). By comparing the predictive capability of such algorithms from published studies, we assessed their potential to augment current clinical gait diagnostics when dealing with knee OA. Methods Thus, an evidence-based review and analysis were conducted. With over 188 studies identified, 8 studies met the inclusion criteria for a subsequent analysis, accounting for 78 participants overall. Results The classification performance of ML and ANN algorithms was quantitatively assessed. The test classification accuracy (ACC), sensitivity (SN), specificity (SP) and area under the curve (AUC) of the ML-based algorithms were clinically valuable, i.e., all higher than 85%, differently from those obtained via ANN. Significance This study demonstrates the potential of ML for clinical assessment of knee disorders in an accurate and reliable manner.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.009 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 source (direct Gemma or distilled Codex), 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".