Develop remote orthotic fabrication workflow using 3D modeling and 3D printing technology for carpometacarpal osteoarthritis
Bibliographic record
Abstract
BACKGROUND: Carpometacarpal (CMC) osteoarthritis often requires orthotic intervention to reduce pain and improve function. Traditional orthosis fabrication typically demands in-person clinical visits, which can be a barrier to care. PURPOSE: To develop and validate a fully remote workflow for fabricating custom orthoses using mobile 3D scanning, web-based assessment, and 3D printing technologies for patients with CMC osteoarthritis. STUDY DESIGN: Pilot validation study. METHODS: A five-step workflow was created: (1) a web-based application ("Hand Scan") for assessing pain, sensory function, and range of motion; (2) mobile 3D hand scanning using smartphone cameras, with scans processed in Agisoft Metashape; (3) digital joint repositioning using finite element analysis software; (4) orthosis design using parametric modeling; and (5) fabrication using 3D printing with Orfit's low-temperature polycaprolactone filament. Five patients with CMC osteoarthritis were recruited. The application's usability was tested via cognitive interviews. Mobile scanning accuracy was validated against high-precision photogrammetry, and surface deviations of the 3D-printed orthoses were compared with traditional thermoplastic models. RESULTS: The Hand Scan app demonstrated strong content validity. Mobile scans showed a mean absolute deviation of 0.93 mm (SD = 0.61 mm). Joint repositioning yielded a mean deviation of 0.87 mm. The 3D-printed orthoses demonstrated a better fit than thermoplastic models, with a mean surface deviation of 0.95 mm compared to 1.96 mm. The maximum deviation was 3.17 mm for 3D-printed and 5.81 mm for traditional orthoses. CONCLUSIONS: This remote orthotic fabrication workflow is accurate, feasible, and clinically applicable. It supports personalized orthosis design while reducing the need for in-person visits. The workflow has strong potential for telehealth and remote hand therapy services.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".