1103 Gait Analysis Using Wearables and AI in Predicting Peripheral Arterial Disease: A Systematic Review
Bibliographic record
Abstract
Abstract Introduction Peripheral arterial disease (PAD) affects over 230 million people globally. The trajectory of this disease process can culminate in major amputation. These patients demonstrate statistically significant changes in gait including changes in stride length, plantarflexion and other spatiotemporal parameters. Aim To determine if alterations in gait parameters can be detected and measured accurately by wearables and AI in PAD patients and if these measurements are predictive of PAD progression. Method A systematic review was performed in line with PRISMA guidelines. MEDLINE, Embase, Web of Science and Scopus were used for database searches. A combination of medical subject headings (MeSH) regarding ‘PAD’, ‘gait’, ‘kinetics’, ‘biomechanics’, ‘ambulation’, ‘artificial intelligence’, ‘sensors’ and ‘smartphone data’ were employed in the primary search string. Screening, full text reviews and data extraction was conducted by 2 reviewers using Covidence. Bias was assessed using the Newcastle-Ottawa scale. Results 4719 studies were identified. 3873 were screened after de-duplication. 5 studies were included in the final review. The main methods of gait measurements included smartphones (iPhone and Samsung Galaxy), wearable sensors and motion sensor technology. Gait initiation parameters were sensitive in detecting gait impairment. iPhone pedometer demonstrated accuracy measuring steps comparable to reference Actigraph but was inaccurate measuring distance. Machine learning models including Random Forest algorithms and extreme gradient boosting, detected PAD patients with 89% and 92% accuracy respectively. Conclusions Wearables and AI algorithms can detect gait changes and differentiate PAD from non-PAD gait. All studies showed detective capability, however none demonstrated predictability of PAD. Limitations include accuracy and validation of measurement methods.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.006 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".