An Interactive Real-Time 3D Representation of a Heart Using a 2D Ultrasound Vest: Proof of Concept
Bibliographic record
Abstract
As the population of older adults increases worldwide, the number of individuals afflicted with cardiovascular issues and diseases is also increasing. The rate at which individuals worldwide succumb to cardiovascular disease (CVD) is rising as well. That is, the World Health Organization (WHO) reports that the number one cause of death globally is from CVD either in the form of myocardial infarctions or strokes. The primary ways of assisting individuals with CVD are from either improved treatments, monitoring research, or primary and secondary prevention measures. In the form of cardiovascular structural monitoring, ultrasonography is very prevalent and allows for multiple configurations, is the least expensive, and has no detrimental side effects to the patient. This is the proof of concept study that investigates how we can combine a wearable ultrasound vest of multiple 2D transducers to create a 3D model of the heart for continuous monitoring. Furthermore, we create functional models to represent the states the heart can be in both respect to normal operations as well as Atrial Fibrillation. Using the wearable ultrasound vest created in our previous work, a 3D model is created via a structure from motion approach with synthetic data. Also, a denoising process is created to assist the modeling process. The 3D model is constructed with up to three views. That is, via the parasternal, frontal, and apical views where the frontal view is the halfway point between the apical and parasternal views. Furthermore, stochastic petri nets (SPN) are created to represent the cyclic states of the heart. The experimental results show a 3D model of the synthetic heart constructed from a point cloud created by the structure from the motion approach. Then, it is successfully denoised with our outlier detection and removal process. The resulting 3D model allows us to calculate surface areas and perform the continuous monitoring we initially set out to do. Finally, multiple SPN models are created for functional feature extraction as well as to assist medical professionals in continuous cardiovascular monitoring. In this paper, we demonstrated the structure from the motion approach to create a 3D model of the heart with our wearable ultrasound vest construction. Furthermore, we provided multiple SPN models for functional feature extraction and to monitor a normal heart and a heart affected by Atrial Fibrillation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".