Head-Mounted Display-Based Augmented Reality for Image-Guided Media Delivery to the Heart: A Preliminary Investigation of Perceptual Accuracy
Bibliographic record
Abstract
By aligning virtual augmentations with real objects, optical see-through head-mounted display (OST-HMD)-based augmented reality (AR) can enhance user-task performance. Our goal was to compare the perceptual accuracy of several visualization paradigms involving an adjacent monitor, or the Microsoft HoloLens 2 OST-HMD, in a targeted task, as well as to assess the feasibility of displaying imaging-derived virtual models aligned with the injured porcine heart. With 10 participants, we performed a user study to quantify and compare the accuracy, speed, and subjective workload of each paradigm in the completion of a point-and-trace task that simulated surgical targeting. To demonstrate the clinical potential of our system, we assessed its use for the visualization of magnetic resonance imaging (MRI)-based anatomical models, aligned with the surgically exposed heart in a motion-arrested open-chest porcine model. Using the HoloLens 2 with alignment of the ground truth target and our display calibration method, users were able to achieve submillimeter accuracy (0.98 mm) and required 1.42 min for calibration in the point-and-trace task. In the porcine study, we observed good spatial agreement between the MRI-models and target surgical site. The use of an OST-HMD led to improved perceptual accuracy and task-completion times in a simulated targeting task.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".