[Early gait analysis after total knee arthroplasty based on artificial intelligence dynamic image recognition].
Bibliographic record
Abstract
OBJECTIVE: To explore early postoperative gait characteristics and clinical outcomes after total knee arthroplasty (TKA). METHODS: ;14 patients on the left side, 12 patients on the right side;according to Kellgren-Lawrence(K-L) classification, 6 patients with grade Ⅲ and 20 patients with grade IV;the courses of disease ranged from 1 to 14 years with an average of (5.54±3.29) years. Images and videos of standing up and walking, walking side shot, squatting and supine kneeling were taken with smart phones before operation and 6 weeks after operation. The human posture estimation framework OpenPose were used to analyze stride frequency, step length, step length, step speed, active knee knee bending angle, stride length, double support phase time, as well as maximum hip flexion angle and maximum knee bending angle on squatting position. Western Ontario and McMaster Universities (WOMAC) arthritis index and Knee Society Score (KSS) were used to evaluate clinical efficacy of knee joint. RESULTS: <0.05). Eleven patients could able to complete squat before operation, 13 patients could able to complete at 6 weeks after operation, and 9 patients could able to complete both before operation and 6 weeks after operation. In 9 patients, the maximum bending angle of crouching position was increased from 76.29° to 124.11° before operation to 91.35° to 134.12° at 6 weeks after operation, and the maximum bending angle of hip was increased from 103.70° to 147.25° before operation to 118.61° to 149.48° at 6 weeks after operation. CONCLUSION: Gait analysis technology based on artificial intelligence image recognition is a safe and effective method to quantitatively identify the changes of patients' gait. Knee pain of KOA was relieved and the function was improved, the supporting ability of the affected limb was improved after TKA, and the patient's stride frequency, stride length and stride speed were improved, and the overall movement rhythm of both lower limbs are more coordinated.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".