How do digital techniques of shape capture and alignment compare to traditional casting methods when applied to pediatric ankle-foot orthoses (AFOs)?
Bibliographic record
Abstract
Achieving optimal alignment and fit is a key aspect of ankle-foot orthosis (AFO) design, as it directly influences the effectiveness of the device. While digital workflows offer the potential to integrate quantifiable alignment measures and corrections into AFO design, a major challenge remains in controlling lower-limb positioning and alignment during 3D scanning. This study aimed to evaluate pediatric AFO alignment and shape differences of directly scanned (live scan) vs casted lower limb models. Eighteen participants aged 4-16 years treated by 5 certified orthotists were recruited. Participants and casts were scanned. Sagittal plane ankle-foot alignment differences were analyzed between pairs of live scan and cast models. Using digital tools, the ankle-foot alignment of the live scans was then corrected, and the alignment differences were re-evaluated to assess the re-alignment methods and allow for further shape comparisons. After correction, modification maps were generated to assess the shape differences (surface deviations) between the live scans and cast models. Shape differences were also assessed with respect to participant characteristics. The results of this study demonstrated that AFO users can be scanned in a nearly corrected position (mean sagittal plane angle difference = 0.85°, SD = 4.44°), and that digital tools can be used to measure and adjust ankle-foot alignment with high accuracy (<1°error). The modification maps revealed that the live scans closely matched the cast models, with shape differences consistently observed in the foot and heel regions. Mean differences ranged -2.12-1.45 mm, positive differences (cast larger) ranged 1.14-2.71 mm, and negative differences (cast smaller) ranged 1.50-3.47 mm. Height, age, and foot length had moderate effects on shape differences (ρ = 0.5-0.75), while significant differences were observed between orthotists (∊2 = 0.32). These findings can drive future advancements in the digital design and fabrication of AFOs.
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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 | 0.012 | 0.071 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".