Visualization of Multimodal Brain Connectivity for Neurosurgical Planning Using Handheld Device Augmented Reality
Bibliographic record
Abstract
In neurosurgical procedures, precise preoperative planning requires extensive knowledge of the patients' anatomy as well as critical structures for brain functionality.Recently, there has been an increase in the use of minimally invasive approaches, owing in part to advancements in multimodal medical imaging techniques such as structural (SC), and functional-based brain mapping (FC), which have been shown to be useful metrics for surgical trajectory planning.The main challenges associated with their use is the lack of intuitive visualization and interactive methods available to neurosurgeons and trainees.AR systems represent a pivotal advancement towards augmenting the training of trainees as well as providing a platform for senior surgeons to maintain their skills in a low-risk training environment.Advanced image processing was performed on multimodal neuroimaging data (T1-weighted image, diffusion weighted image, resting-state functional magnetic resonance imaging) to characterize the SC and FC of the brain.An AR application, NeuroAR, was designed to take these as inputs and allow the user to visualize and interact with the neuroanatomy in the context of its associated SC and FC.The performances of 10 users on 24 targets were evaluated using an extension of Fitts' methodology.The users were able to use an interactive tool to select and visualize brain regions and their associated fibers.The fibers could be visualized based on their FC scores.As expected, the data showed that task difficulty increased as the volume of the fibers decreased.Movement time also increased as task difficulty increased.We introduced a new mobile device AR application based on data derived from advanced image processing of neuroimaging data.Evaluation of the 3D pointing tasks showed consistency in user performance indicating its utility.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| 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 | 0.000 |
| Insufficient payload (model declined to judge) | 0.006 | 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".