3D cinematic reconstructions of cardiovascular CT presented in augmented reality: subjective assessment of clinical feasibility and potential use cases
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
Augmented reality (AR) is a new technique enabling interaction with three-dimensional (3D) holograms of cinematic rendering (CR) reconstructions. Research in this field is in its very early steps, and data is scarce. We evaluated image quality, usability, and potential applications of AR in cardiovascular image datasets. Ten CR reconstructions of cardiovascular computed tomography (CT) datasets with complex anatomical abnormalities were presented to six radiologists and three cardiologists first on diagnostic screens and subsequently in AR. Subjective image quality and user experience were rated on 5-point Likert scales to assess usability and potential applications of AR. CR of CT datasets covering multiple images series of the same exam with differing kernels was performed in 143 ± 31 s (mean ± standard deviation); reconstruction of single CT image series took 84 ± 30 s. Mean subjective image quality was excellent, and observers showed high endorsement of the intuitive usability of the AR device and improvement of anatomical comprehensibility. AR devices were expected to have the greatest impact on patient and student education as well as multidisciplinary discussions, with less potential in clinical care. Clinical testing and preclinical implementation of AR seem feasible due to reasonable computation times and intuitive usability even for first-time users. RELEVANCE STATEMENT: The presentation of 3D cinematic rendering in augmented reality provides excellent image quality, facilitating the comprehension of anatomical structures in CT datasets. Concurrently, reasonable computation times and the intuitive usability of augmented reality devices make preclinical implementation and clinical testing feasible. KEY POINTS: 3D cinematic reconstructions presented in augmented reality improve the anatomical comprehensibility of chest CT scans. Augmented reality devices are expected to be highly beneficial in educational settings and multidisciplinary discussions. Usability and computation times are feasible for initial preclinical use cases.
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 | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it