Accuracy and Reliability of 3D Imaging for Facial Movement Evaluation: Validation of the VECTRA H1
Bibliographic record
Abstract
Three-dimensional imaging can be used to obtain objective assessments of facial morphology that is useful in a variety of clinical settings. The VECTRA H1 is unique in that it is relatively inexpensive, handheld, and does not require standardized environmental conditions for image capture. Although it provides accurate measurements when imaging relaxed facial expressions, the clinical evaluation of many disorders involves the assessment of facial morphology when performing facial movements. The aim of this study was to assess the accuracy and reliability of the VECTRA H1, specifically when imaging facial movement. Methods: The accuracy, intrarater, and interrater reliability of the VECTRA H1 were assessed when imaging four facial expressions: eyebrow lift, smile, snarl, and lip pucker. Fourteen healthy adult subjects had the distances between 13 fiducial facial landmarks measured at rest and the terminal point of each of the four movements by digital caliper and by the VECTRA H1. Intraclass correlation and Bland-Altman limits of agreement were used to determine agreement between measures. The agreement between measurements obtained by five different reviewers was evaluated by intraclass correlation to determine interrater reliability. Results: Median correlation between digital caliper and VECTRA H1 measurements ranged from 0.907 (snarl) to 0.921 (smile). Median correlation was very good for both intrarater (0.960-0.975) and interrater reliability (0.997-0.999). The mean absolute error between modalities, and both within and between raters was less than 2 mm for all movements tested. Conclusion: The VECTRA H1 met acceptable standards for the assessment of facial morphology when imaging facial movements.
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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 | 0.019 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".