Projected Augmented Reality to Display Medical Information Directly on a Patient’s Skin
Bibliographic record
Abstract
A patient’s internal anatomy can be difficult to visualize when viewed on a monitor, head-mounted display, or even when looking at an actual patient. Combining medical images (CT, MRI, US, PET) with a physical model helps recover missing anatomical context and improves situational awareness. This chapter describes an augmented reality system capable of projecting medical image information directly onto curved targets such as the human body or a mannequin. The motion of the targets and the projector are tracked using a motion capture system so that the images are adjusted in real time to match the anatomy changes in position and orientation. The augmented information can be displayed using volume rendering for realistic visualization of the internal anatomy and 3D models from segmented images. Calibration is performed on the projector and the tracking system to obtain an accurate, common coordinate system and correct visual distortions created by the fact that the projected screen (human body) is no longer a plane. The system is easily extendable to other display technology and has many potential applications, including medical education, surgical planning, and laparoscopic surgery.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.054 | 0.019 |
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".