Gesture-controlled interactive three dimensional anatomy: A novel teaching tool in head and neck surgery
Bibliographic record
Abstract
BACKGROUND: There is a need for innovative anatomic teaching tools. This paper describes a three dimensional (3D) tool employing the Microsoft Kinect™. Using this instrument, 3D temporal bone anatomy can be manipulated with the use of hand gestures, in the absence of mouse or keyboard. METHODS: CT Temporal bone data is imported into an image processing program and segmented. This information is then exported in polygonal mesh format to an in-house designed 3D graphics engine with an integrated Microsoft Kinect™. Motion in the virtual environment is controlled by tracking hand position relative to the user's left shoulder. RESULTS: The tool successfully tracked scene depth and user joint locations. This permitted gesture-based control over the entire 3D environment. Stereoscopy was deemed appropriate with significant object projection, while still maintaining the operator's ability to resolve image details. Specific anatomical structures can be selected from within the larger virtual environment. These structures can be extracted and rotated at the discretion of the user. Voice command employing the Kinect's™ intrinsic speech library was also implemented, but is easily confounded by environmental noise. CONCLUSION: There is a need for the development of virtual anatomy models to complement traditional education. Initial development is time intensive. Nonetheless, our novel gesture-controlled interactive 3D model of the temporal bone represents a promising interactive teaching tool utilizing a novel interface.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".