An interactive augmented reality software for facial reconstructive surgeries.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVE: Surgical trainees need a lot of training and practice before being able to operate independently. The current approach of surgical education mainly involves didactic teaching and psychomotor training through physical models or real tissue. Due to the unavailability of physical resources and lack of objective ways of evaluation, there is a demand for developing alternative training methods for surgeons. In this paper, we present an application that provides additional training opportunities to surgical trainees in the field of facial reconstructive surgeries. METHODS: We built a mobile augmented reality application that helps the user to visualize important concepts and experiment with different surgical plans for facial reconstructive surgeries. The application can overlay relaxed skin tension lines on a live video input or a patient's photo, which serve as bases for aligning a skin flap. A surgical trainee can interactively compare different skin flap design choices with estimated final scars on a photo of a patient. Data collection capability is also added to the application, and we performed a Monte Carlo experiment with simulated users (five classes of 100 users each) as an example of objectively measuring user performance. RESULTS: The application can overlay relaxed skin tension lines on a patient's face in real time on a modern mobile device. Accurate overlays were achieved in over 91% as well as 84% and 88% out of 263 generated face images, depending on the method. Visual comparisons of the three overlay methods are presented on sample faces from different population groups. From the Monte Carlo experiment, we see that user actions in each class follow a normal distribution with a distinct set of parameters. CONCLUSIONS: This application can serve as a basis for teaching surgical trainees the fundamentals of different facial reconstructive procedures, especially concepts related to relaxed skin tension lines and skin flaps. It can objectively evaluate the performance of surgical trainees in a course. This setup focuses on illustrating the relationship between the orientation of skin flaps and relaxed skin tension lines, which is a prerequisite of minimizing scarring in patients in addition to good surgical techniques.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.021 | 0.004 |
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".