A smartphone photogrammetry method for digitizing prosthetic socket interiors
Bibliographic record
Abstract
BACKGROUND AND AIM: Prosthetic CAD/CAM systems require accurate 3D limb models; however, difficulties arise when working from the person's socket since current 3D scanners have difficulties scanning socket interiors. While dedicated scanners exist, they are expensive and the cost may be prohibitive for a limited number of scans per year. A low-cost and accessible photogrammetry method for socket interior digitization is proposed, using a smartphone camera and cloud-based photogrammetry services. TECHNIQUE: 15 two-dimensional images of the socket's interior are captured using a smartphone camera. A 3D model is generated using cloud-based software. Linear measurements were comparing between sockets and the related 3D models. DISCUSSION: 3D reconstruction accuracy averaged 2.6 ± 2.0 mm and 0.086 ± 0.078 L, which was less accurate than models obtained by high quality 3D scanners. However, this method would provide a viable 3D digital socket reproduction that is accessible and low-cost, after processing in prosthetic CAD software. Clinical relevance The described method provides a low-cost and accessible means to digitize a socket interior for use in prosthetic CAD/CAM systems, employing a smartphone camera and cloud-based photogrammetry software.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".