Feasibility and accuracy of real-time 3D-holographic graft length measurements
Bibliographic record
Abstract
Abstract Aims Mixed reality (MR) holograms can display high-definition images while preserving the user’s situational awareness. New MR software can measure 3D objects with gestures and voice commands; however, these measurements have not been validated. We aimed to assess the feasibility and accuracy of using 3D holograms for measuring the length of coronary artery bypass grafts. Methods and results An independent core lab analyzed follow-up computer tomography coronary angiograms performed 30 days after coronary artery bypass grafting in 30 consecutive cases enrolled in the FASTTRACK CABG trial. Two analysts, blinded to clinical information, performed holographic reconstruction and measurements using the CarnaLife Holo software (Medapp, Krakow, Poland). Inter-observer agreement was assessed in the first 20 cases. Another analyst performed the validation measurements using the CardIQ W8 CT system (GE Healthcare, Milwaukee, Wisconsin). Seventy grafts (30 left internal mammary artery grafts, 31 saphenous vein grafts, and 9 right internal mammary artery grafts) were measured. Holographic measurements were feasible in 97.1% of grafts and took 3 minutes 36 s ± 50.74 s per case. There was an excellent inter-observer agreement [interclass correlation coefficient (ICC) 0.99 (0.97–0.99)]. There was no significant difference between the total graft length on hologram and CT [187.5 mm (157.7–211.4) vs. 183.1 mm (156.8–206.1), P = 0.50], respectively. Hologram and CT measurements are highly correlated (r = 0.97, P < 0.001) with an excellent agreement [ICC 0.98 (0.97–0.99)]. Conclusion Real-time holographic measurements are feasible, quick, and accurate even for tortuous bypass grafts. This new methodology can empower clinicians to visualize and measure 3D images by themselves and may provide insights for procedural strategy.
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 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 | 0.017 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".