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Record W4390724911 · doi:10.1109/access.2024.3351803

High-Resolution Stereoscopic Visualization of Pediatric Echocardiography Data on Microsoft HoloLens 2

2024· article· en· W4390724911 on OpenAlexafffund
Meruja Selvamanikkam, Michelle Noga, Nee Scze Khoo, Kumaradevan Punithakumar

Bibliographic record

VenueIEEE Access · 2024
Typearticle
Languageen
FieldComputer Science
TopicAugmented Reality Applications
Canadian institutionsUniversity of Alberta
FundersChildren's Hospital FoundationStollery Children’s Hospital FoundationWomen and Children's Health Research InstituteNatural Sciences and Engineering Research Council of CanadaChildren's Health Research Institute
KeywordsComputer scienceVolume renderingVisualizationStereoscopyRendering (computer graphics)Computer graphics (images)Augmented realitySoftwareComputer visionMarching cubesVirtual realityVoxelMedical simulation3D renderingArtificial intelligenceSimulation

Abstract

fetched live from OpenAlex

Three-dimensional ultrasound offers volumetric images and detailed anatomical data for medical diagnosis and treatment planning. It is a key tool in the medical field to obtain a comprehensive view of the body. Ordinary two-dimensional displays do not provide depth perception and are not suitable for representing volumetric data, necessitating the use of more sophisticated visualization methods. Virtual and augmented reality (AR) displays can be used to improve the visualization of medical images, allowing for more natural interaction with the environment. This study proposes custom software developed using the Unity3D platform to render high-resolution 3D echocardiography (3DE) on the Microsoft HoloLens 2, providing an immersive AR experience for medical professionals. This research focuses on three-dimensional echocardiography in children and uses a phantom heart model to mimic a pulsating heart. The volume rendering algorithm utilizes the ray-marching technique, enabling direct volume rendering of high-quality volumetric models. To maintain a satisfactory frame rate, a Holographic Remoting approach is employed to reduce latency and enhance network transmission speed, utilizing the resources of a personal computer (PC). The custom software developed offers an intuitive and interactive user interface that allows medical professionals to manipulate and explore 3DE images effectively. The interaction includes the ability to slice, modify the intensity range, and alter the voxel density. The experimental evaluations demonstrated that it is possible to produce high-quality real-time display with HoloLens 2 and a PC-based remote rendering system, allowing intuitive control and exploration of 3DE. Overall, this research highlights the potential of AR rendering offered through Microsoft HoloLens 2 to advance pediatric 3DE rendering for medical professionals to enhance their decision-making and understanding of medical datasets.

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.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation 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: none
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0070.001

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.

Opus teacher head0.055
GPT teacher head0.356
Teacher spread0.300 · 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

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
Domainnot available
GenreEmpirical

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".

Quick stats

Citations4
Published2024
Admission routes2
Has abstractyes

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