A Novel 3D Camera-Based ECG-Imaging System for Electrode Position Discovery and Heart-Torso Registration
Bibliographic record
Abstract
ECG-imaging has been receiving increasing clinical and commercialization interest as a non-invasive technology for computing cardiac electrical activity and facilitating clinical management of heart rhythm disorders. Despite its potential, the standard ECG-imaging pipeline requires thorax imaging for constructing patient-specific heart-thorax geometry - being outside standard-of-care clinical workflow, this component constitutes a major barrier to the clinical adoption of ECG-imaging. The advent of 3D cameras into ECG-imaging workflow to replace thorax imaging has shown promise, although existing works largely neglect the registration between camera-derived thorax models with an individual's heart geometry. In this work, we address this gap with a novel system that generates patient-specific torso geometry using a 3D camera, registers the torso to heart geometry obtained from pre-existing cardiac scans, and optimally deforms the torso to the skin surface of the patient. We evaluated the presented camera-based ECG-imaging system on five patients undergoing ablation of scar-related ventricular tachycardia, where we evaluate both the accuracy of the surface electrode localization and the ECG-imaging solutions in comparison to those obtained from computed tomography based thorax imaging. We further evaluate the use of the presented camera-based patient-specific heart-thorax model versus a generic heart-thorax model, highlighting the importance of the registration between the camera-based thorax models with cardiac scans.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| 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 | 0.001 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".