3D reconstruction of the human trunk for designing personalized braces : Precision study
Bibliographic record
Abstract
The customized design of braces for adolescent idiopathic scoliosis (AIS) treatment requires the acquisition of the 3D external geometry of the patients' trunks. Three body scanning systems are available at CHU Sainte-Justine in Montreal: a fixed system of InSpeck Capturor II LF digitizers and two portable scanners, BodyScan and Structure Sensor. The aim of this study is to compare them by evaluating their accuracy and repeatability. To achieve this, we placed 46 surface markers on an anthropomorphic manikin and scanned it three times with each system. We also measured the 3D coordinates of the same markers using a coordinate measuring machine (CMM), serving as ground-truth. We evaluated the repeatability and accuracy of the three systems: the former, by measuring the bidirectional mean distance between the three surfaces acquired with a given modality; the latter, by calculating the residual normal distance separating each of the 3D surfaces and the CMM point cloud. We also compared texture mapping accuracy between InSpeck and Structure Sensor by examining the CMM point cloud versus the marker 3D coordinates selected on the trunk surface. The results show good accuracy and repeatability for all three systems, with slightly better geometric accuracy for BodyScan (p-value ≈ 10-6). In terms of texture mapping, InSpeck showed better accuracy than Structure Sensor (p-value = 0.0059).
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".