Heightening Surgical Education and Virtual Reality Simulation of the Pterygopalatine Fossa through Photorealism
Bibliographic record
Abstract
The neurovascular anatomy and the spatial orientation of the contents of the pterygopalatine fossa can be difficult to conceptually understand, especially for trainees. The lower frequency of real case scenarios where the pterygopalatine fossa is exposed, coupled with its challenging anatomy and limited learning resources make this region of the skull base an enigmatic area. We aimed to develop a surgical education app with multidisciplinary relevance to neurosurgery and otolaryngology trainees. Photorealistic techniques were applied to open-source, high fidelity illustrations of the pterygopalatine fossa. Paired with a high-resolution computed tomography scan of the human head to render the endonasal corridor, we elucidate the surgically relevant anatomy of this critical space in the context of the expanded endonasal transpterygoid approach to the pterygopalatine fossa. Blender, a three-dimensional modelling and rendering software, was employed for creating accurate three-dimensional anatomy, encompassing not only its sculpting tools but also its broader feature set. Lighting and textures were achieved through Sketchfab's capabilities, leveraging its platform for rendering and showcasing the three-dimensional models. Accurate rendering of the contents, foramina and boundaries was developed with expert surgeons and endoscopic anatomy photos. This virtual reality simulation facilitates 'just in time' learning at the fingertips of trainees in an interactive and immersive manner. We aim to augment surgical education via an accessible user interface that can accompany trainees to the operating room.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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".