Predicting recovery after knee replacement using smart insoles and AI
Prediction of Patient-reported Outcome Measure and Performance-based Measure After Total Knee Arthroplasty Using Instrumented Insoles and Deep Neural Networks
This study will test whether preoperative clinical information and walking data from instrumented insoles can help predict recovery six weeks after total knee replacement for adults with knee osteoarthritis.
Quick facts
| Study type | Observational |
|---|---|
| Enrollment | 200 (estimated) |
| Ages | 19 Years and up |
| Sex | All |
| Sponsor | Yonsei University Academic / other |
| Locations | 1 site (Yongin-si, Gyeonggi-do) |
| Trial ID | NCT07367789 on ClinicalTrials.gov |
What this trial studies
This multicenter prospective observational study will collect preoperative demographic data, body composition, muscle strength, comorbidity profiles, Kellgren-Lawrence radiographic grade, and gait data from instrumented insoles during the Timed Up and Go Test. Participants will complete validated patient-reported outcome measures (WOMAC) and performance-based mobility tests before surgery and at six weeks after surgery. Prediction models including linear regression, random forest, and deep neural networks will be trained using the same input variables and compared using accuracy and ROC-AUC. The protocol involves only gait assessments and questionnaires and is expected to pose minimal additional risk.
Who should consider this trial
Good fit: Adults aged 19 or older with radiographic knee osteoarthritis scheduled for primary total knee arthroplasty who can walk independently (FAC ≥ 3) at the participating hospitals are ideal candidates.
Not a fit: Patients who are non-ambulatory, have a prior TKA on the same knee, have neurological or musculoskeletal disorders that affect gait, are pregnant, or cancel their surgery are unlikely to benefit from the predictive models.
Why it matters
Potential benefit: If successful, the approach could provide individualized predictions of short-term improvement after TKA to help guide patient expectations and rehabilitation planning.
How similar studies have performed: Previous work using wearable sensors and machine-learning has shown promise for predicting mobility and patient-reported outcomes, but combining instrumented insoles with deep neural networks specifically for TKA recovery is relatively novel.
Eligibility criteria
Show full inclusion / exclusion criteria
Inclusion Criteria: * Age ≥ 19 years * Diagnosed with knee osteoarthritis (Kellgren-Lawrence grade 1-4) * Scheduled for primary Total Knee Arthroplasty (TKA) at either study site * Able to walk independently on level ground (Functional Ambulation Categories, FAC ≥ 3) Exclusion Criteria: * Cancellation of scheduled TKA * History of TKA on the same knee * Neurological or musculoskeletal disorders affecting gait * Pregnancy or possibility of pregnancy * Any condition deemed inappropriate for study participation by the investigator
Where this trial is running
Yongin-si, Gyeonggi-do
- Yongin Severance Hospital — Yongin-si, Gyeonggi-do, South Korea (Recruiting)
Study contacts
- Principal investigator: Na Young Kim, MD, PhD — Severance Hospital
- Study coordinator: Na Young Kim, MD, PhD
- Email: kny8452@yuhs.ac
- Phone: +82 010 9127 4482
How to participate
- Review the eligibility criteria above with your treating physician.
- Visit the official trial page on ClinicalTrials.gov for the most current contact information and recruitment status.
- Contact the listed study coordinator or principal investigator to request pre-screening. Pre-screening is free and never obligates you to enroll.