Identifying the third dimension in 2D fluoroscopy to create 3D cardiac maps
Bibliographic record
Abstract
Three-dimensional cardiac mapping is important for optimal visualization of the heart during cardiac ablation for the treatment of certain arrhythmias. However, many hospitals and clinics worldwide cannot afford the high cost of the current mapping systems. We set out to determine if, using predefined algorithms, comparable 3D cardiac maps could be created by a new device that relies on data generated from single-plane fluoroscopy and patient recording and monitoring systems, without the need for costly equipment, infrastructure changes, or specialized catheters. The study included phantom and animal experiments to compare the prototype test device, Navik 3D, with the existing CARTO 3 System. The primary endpoint directly compared: (a) the 3D distance between the Navik 3D-simulated ablation location and the back-projected ground truth location of the pacing and mapping catheter electrode, and (b) the same distance for CARTO. The study's primary objective was considered met if the 95% confidence lower limit was greater than 0.75% for the Navik 3D-CARTO difference between the 2 distances, or less than or equal to 2 mm. Study results showed that the Navik 3D performance was equivalent to the CARTO system, and that accurate 3D cardiac maps can be created using data from equipment that already exists in all electrophysiology labs.
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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 | 0.002 | 0.011 |
| 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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".