Acoustic Emission Biomarkers for the Detection and Monitoring of Early Knee Osteoarthritis: Protocol for a Prospective, Single-Center, Exploratory Study
Bibliographic record
Abstract
Background: Osteoarthritis is a highly prevalent and disabling condition. In early stages, patients are asymptomatic or only experience activity-related pain. When pain intensifies, the disease has often progressed, with few treatment options besides knee arthroplasty. Recently, there has been a growing interest in acoustic emissions generated by poorly lubricated and/or damaged moving joint surfaces, as seen in osteoarthritis. Noninvasive analysis of knee sound could help identify patients with early-stage osteoarthritis at a low cost, without radiation exposure. Thus, preventive measures could be implemented earlier and delay the progression of osteoarthritis. Objective: This study aims to identify acoustic biomarkers with prognostic value for the early detection of osteoarthritis. To achieve this, a preliminary reference database will be developed using a subpopulation at risk of developing osteoarthritis, incorporating acoustic signals and gold-standard clinical measures for validation. Methods: A total of 100 patients with previous reconstructive knee surgery and 20 healthy controls will be examined twice at a 9-month interval for both knees. Acoustic emissions (AE) will be recorded with the Inmodi knee brace (École Polytechnique Fédérale de Lausanne; EPFL) during 4 functional tests (unloaded flexion-extension, sit-to-stand test, one-step test, and walk test). Gold-standard osteoarthritis diagnostics will be assessed and evaluated by an experienced radiologist for (1) the magnetic resonance imaging (MRI) Osteoarthritis Knee Score (MOAKS) and (2) the Kellgren-Lawrence (KL) grading scores. Feature analysis and multivariate modeling will be used to study the association between the extracted sound parameters and osteoarthritis (MOAKS and KL). In addition, thermal images will be taken to investigate the state of inflammation. Participants will fill out 5 health-related questionnaires (Oxford Knee Score [OKS], Core Outcome Measures Index [COMI], University of California at Los Angeles [UCLA ]activity scale, Knee Injury and Osteoarthritis Outcome Score [KOOS], and Forgotten Joint Knee Score-12 [FJS-12]) at both time points. Results: Recruitment and data collection started in December 2022. In November 2023, data collection of all 120 participants (female: n=49, 41%) was completed for the first visit. The data collection for the second visit will end in 2024. Preliminary data processing and analysis are ongoing at the time of this writing. Conclusions: This exploratory study will contribute to a better understanding of the disease progression of osteoarthritis and the use of acoustic biomarkers for predicting osteoarthritis.
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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 | 0.029 | 0.020 |
| Meta-epidemiology (narrow) | 0.005 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.022 | 0.007 |
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".