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Record W4406808063 · doi:10.1142/s0218213025500046

An Interactive Real-Time 3D Representation of a Heart Using a 2D Ultrasound Vest: Proof of Concept

2025· article· en· W4406808063 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueInternational Journal of Artificial Intelligence Tools · 2025
Typearticle
Languageen
FieldMedicine
TopicSurgical Simulation and Training
Canadian institutionsArtificial Intelligence in Medicine (Canada)
Fundersnot available
KeywordsVESTProof of conceptRepresentation (politics)Computer scienceComputer graphics (images)Computer visionComputer security

Abstract

fetched live from OpenAlex

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.

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.

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.342
Threshold uncertainty score0.616

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.099
GPT teacher head0.435
Teacher spread0.336 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it