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. Image 1. Views of the pterygopalatine fossa simulation through a left endonasal transpterygoid approach after a left medial maxillotomy. (A) Left middle turbinate (MT) and ethmoid bulla (EB) in relation to the pterygopalatine fossa for orientation. (B) A deeper view through with the posterior wall of the maxillary sinus removed revealing the pterygopalatine fossa and its contents. Users can zoom in and rotate to peer through foramina. Image 2. A view of the pterygopalatine fossa simulation from a lateral vantage point through the pterygomaxillary fissure. Image 3. The left pterygopalatine fossa virtual simulation. In this immersive interface, users have unlimited degrees of freedom to rotate, zoom in and zoom out to conceptualize the spatial relationships of the neurovascular contents and foramina. Publication History Article published online: 05 February 2024 © 2024. Thieme. All rights reserved. Georg Thieme Verlag KG Rüdigerstraße 14, 70469 Stuttgart, Germany
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.003 |
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".