An assistive photo capture system for pre- and post-operative analysis of facial reconstructive surgeries
Bibliographic record
Abstract
It is common for facial reconstructive surgeons to take pre- and post-operative images from patients to keep track of their healing progress. However, current guidelines only focus on hardware and lighting setup. Therefore, most pre- and post-operative images are taken from different perspectives. This makes them not suitable for quantitative analysis such as comparing with simulation results, as it is very difficult to compare paths and distances in two two-dimensional face photos taken from vastly different perspectives. To address this issue, we propose an application to ensure the pre- and post-operative images are taken from the same perspective. We build a mobile application where we first record the face pose of the pre-operative image. When taking the post-operative image, we compare the face pose of the current frame with the pre-operative pose and only take a photo when the difference is below a threshold. We performed a comparison study of taking post-operative images with the proposed application and the phone camera on six head models. Experimental results show that the alignment error for the proposed application is only 1/3 of that of the phone camera, proving the effectiveness of our system.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".