Feasibility of Skull Base Neuronavigation Using Optical Topographic Imaging Toward Guidance of Human and Robotic Drill Operators
Bibliographic record
Abstract
Mastoidectomy is a surgical procedure that allows access to critical portions of the skull base in order to treat a broad range of conditions. Mastoidectomy requires enormous precision and focus in order to avoid critical structures such as veins or nerves, and often occurs at the beginning of long, complex skull base operations, leading to cognitive and physical fatigue before some of the most crucial portions of a surgery have occurred. Neuronavigation, the technique of using preoperative imaging and patient anatomy to generate a surgical roadmap, is often used to facilitate safe mastoidectomy in current clinical practice. Optical topographical imaging (OTI) is a form of neuronavigation that uses structured light projected onto the operative surface to facilitate co-registration of 3D space with preoperative imaging. This thesis endeavored to explore whether OTI-assisted neuronavigation could be combined with multi-parameter, realtime data collection of the mechanical parameters of mastoidectomy, with the ultimate goal of developing a platform to be used in the future for robotic mastoidectomy. First, a comprehensive review of the current body of literature surrounding robotic mastoidectomy revealed that previous investigators had not yet quantified the mechanical parameters of mastoidectomy. Our next step was to compare both OTI-assisted and free-hand mastoidectomy, which revealed that OTI-assistance led to shorter procedure times. This set of experiments also demonstrated that OTI navigation was accurate in the skull base, while highlighting room for improvement of the accuracy of OTI navigation. Following this, we used an iterative design approach to develop a platform for multi-parameter, real-time data collection during the drilling. This platform allows for the collection of data ranging from drill tip movement to force magnitude in all three dimensions throughout mastoidectomy. Ultimately, this body of work serves to advance the goal of robotic OTI-assisted mastoidectomy by demonstrating the feasibility of OTI-assisted neuronavigation in the lateral skull base and developing a platform which can be used to create a reference database of the mechanical parameters of mastoidectomy, effectively serving to quantify a previously qualitative process. Future projects may use this database, in combination with patient-specific preoperative imaging, to train roboticalgorithms to perform mastoidectomy.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".