Developing a virtual reality simulation system for preoperative planning of thoracoscopic thoracic surgery
Bibliographic record
Abstract
BACKGROUND: Video-assisted thoracoscopic surgery (VATS) has become a standard approach for the treatment of lung cancer. However, its minimally invasive nature limits the field of view and reduces tactile feedback. These limitations make it vital that surgeons thoroughly familiarize themselves with the patient's anatomy preoperatively. We have developed a virtual reality (VR) surgical navigation system using head-mounted displays (HMD). The aim of this study was to investigate the potential utility of this VR simulation system in both preoperative planning and intraoperative assistance, including support during thoracoscopic sublobar resection. METHODS: Three-dimensional (3D) polygon data derived from preoperative computed tomography data was loaded into BananaVision software developed at Colorado State University and displayed on an HMD. An interactive 3D reconstruction image was created, in which all the pulmonary structures could be individually imaged. Preoperative resection simulations were performed with patient-individualized reconstructed 3D images. RESULTS: The 3D anatomic structure of pulmonary vessels and a clear vision into the space between the lesion and adjacent tissues were successfully appreciated during preoperative simulation. Surgeons could easily evaluate the real patient's anatomy in preoperative simulations to improve the accuracy and safety of actual surgery. The VR software and HMD allowed surgeons to visualize and interact with real patient data in true 3D providing a unique perspective. CONCLUSIONS: This initial experience suggests that a VR simulation with HMD facilitated preoperative simulation. Routine imaging modalities combined with VR systems could substantially improve preoperative planning and contribute to the safety and accuracy of anatomic resection.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 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".