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Record W4324129800 · doi:10.1111/1754-9485.13518

Cinematic versus volume rendered imaging for the depiction of complex congenital heart disease

2023· article· en· W4324129800 on OpenAlexaff
Natalie Layden, Colm Brassil, Nihar Jha, Jelena Saundankar, Deane Yim, David Andrews, Aditya Patukale, Shrivuthsun Srigandan, Conor Murray

Bibliographic record

VenueJournal of Medical Imaging and Radiation Oncology · 2023
Typearticle
Languageen
FieldEngineering
TopicAnatomy and Medical Technology
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsVolume renderingMedicineRendering (computer graphics)Heart diseaseVisualizationRadiologyPerceptionArtificial intelligenceComputer sciencePathologyPsychology

Abstract

fetched live from OpenAlex

INTRODUCTION: Planning for surgical intervention for patients with complex congenital heart disease requires a comprehensive understanding of the individual's anatomy. Cinematic rendering (CR) is a novel technique that purportedly builds on traditional volume rendering (VR) by converting CT image data into clearly defined 3D reconstructions through the stimulation and propagation of light rays. The purpose of this study was to compare CR to VR for the understanding of critical anatomy in unoperated complex congenital heart disease. METHODS: In this retrospective study, CT data sets from 20 sequential scanned cases of unoperated paediatric patients with complex congenital heart disease were included. 3D images were produced at standardised and selected orientations, matched for both VR and CR. The images were then independently reviewed by two cardiologists, two radiologists and two surgeons for overall image quality, depth perception and the visualisation of surgically relevant anatomy, the coronary arteries and the pulmonary veins. RESULTS: Cinematic rendering demonstrated significantly superior image quality, depth perception and visualisation of surgically relevant anatomy than VR. CONCLUSION: Cinematic rendering is a novel 3D CT-rendering technique that may surpass the traditionally used volumetric rendering technique in the provision of actionable pre-operative anatomical detail for complex congenital heart disease.

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 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)0.001
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: Simulation or modeling · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.785
Threshold uncertainty score0.188

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
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)0.0000.000

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 head0.021
GPT teacher head0.325
Teacher spread0.303 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designSimulation or modeling
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

Citations5
Published2023
Admission routes1
Has abstractyes

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