Augmented Reality Image Guidance Improves Navigation for Beating Heart Mitral Valve Repair
Bibliographic record
Abstract
OBJECTIVE: Emerging off-pump beating heart valve repair techniques offer patients less invasive alternatives for mitral valve (MV) repair. However, most of these techniques rely on the limited spatial and temporal resolution of transesophageal echocardiography (TEE) alone, which can make tool visualization and guidance challenging. METHODS: Using a magnetic tracking system and integrated sensors, we created an augmented reality (AR) environment displaying virtual representations of important intracardiac landmarks registered to biplane TEE imaging. In a porcine model, we evaluated the AR guidance system versus TEE alone using the transapically delivered NeoChord DS1000 system to perform MV repair with chordal reconstruction. RESULTS: Successful tool navigation from left ventricular apex to MV leaflet was achieved in 12 of 12 and 9 of 12 (P = 0.2) attempts with AR imaging and TEE alone, respectively. The distance errors of the tracked tool tip from the intended midline trajectory (5.2 ± 2.4 mm vs 16.8 ± 10.9 mm, P = 0.003), navigation times (16.7 ± 8.0 seconds vs 92.0 ± 84.5 seconds, P = 0.004), and total path lengths (225.2 ± 120.3 mm vs 1128.9 ± 931.1 mm, P = 0.003) were significantly shorter in the AR-guided trials compared with navigation with TEE alone. Furthermore, the potential for injury to other intracardiac structures was nearly 40-fold lower when using the AR imaging for tool navigation. The AR guidance also seemed to shorten the learning curve for novice surgeons. CONCLUSIONS: Augmented reality-enhanced TEE facilitates more direct and safe intracardiac navigation of the NeoChord DS tool from left ventricular apex to MV leaflet. Tracked tool path results demonstrate fourfold improved accuracy, fivefold shorter navigation times, and overall improved safety with AR imaging guidance.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 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 | 0.000 | 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".