An Immersive Virtual Reality Environment for Diagnostic Imaging
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Purpose: Advancements in and adoption of consumer virtual reality (VR) are currently being propelled by numerous upcoming devices such as the Oculus Rift. Although applications are currently growing around the entertainment field, wide-spread adoption of VR devices opens up the potential for other applications that may have been unfeasible with past implementations of VR. A VR environment may provide an equal or larger screen area than what is provided with the use of multiple conventional displays while remaining comparatively cheaper and more portable making it an attractive option for diagnostic radiology applications. Methods A VR application for the viewing of multiple image slices was designed using: the Oculus Rift head-mounted display (HMD), Unity, and 3D Slicer. Volumes loaded within 3D Slicer are sent to a Unity application that proceeds to render a scene for the Oculus Rift HMD. Users may interact with the images adjusting windowing and leveling using a handheld gamepad controller. Multiple images may be brought closer to the user for detailed inspection. Results Application usage was demonstrated with the simultaneous visualization of longitudinal slices of a serial CT scan of a patient with a lung nodule. Pilot studies for validating usage of the VR system for differential diagnosis and remote collaboration were performed. Initial results suggest that using the VR system increased both task load and time taken to complete tasks, however, the resulting accuracy in assessing nodule growth of nodules was not significantly different than that achieved using a DICOM viewer application on a traditional display.
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.
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 | 0.006 | 0.014 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 it