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Record W2771726357 · doi:10.15353/vsnl.v3i1.163

Integrating Multispectral Hemodynamic Imaging for Bulk Tissue Oxygenation Analysis

2017· article· en· W2771726357 on OpenAlexfundvenueno aff
Megan Y Y Tran, Robert Amelard, Alexander Wong

Bibliographic record

VenueJournal of Computational Vision and Imaging Systems · 2017
Typearticle
Languageen
FieldMedicine
TopicOptical Imaging and Spectroscopy Techniques
Canadian institutionsnot available
FundersNatural Sciences and Engineering Research Council of CanadaCanada Research ChairsAGE-WELL
KeywordsMultispectral imageOxygenationBiomedical engineeringPerfusionHemodynamicsCuffMaterials scienceMedicineComputer scienceArtificial intelligenceCardiologyInternal medicineSurgery

Abstract

fetched live from OpenAlex

Tissue perfusion and oxygenation are important factors in predicting patient outcomes, but current non-invasive devices for this type of measurement are limited to contact-based single-site monitoring. We present the co-integration of a multispectral optical-electronic subsystem into an existing non-contact coded hemodynamic imaging (CHI) device to enable image acquisition under different illuminants for spatial tissue oxygenation. Stability of the optical output for three illuminants over 10 mins was validated by the imaging system, with σmax=0.407 intensity units, reflecting stability in local fluctuations, and a maximal overall change of 3.1 units. Bulk tissue oxygenation measurement of the thenar eminence during a cuff occlusion experiment revealed relative changes in absorbance due to oxy- and deoxyhemoglobin consistent with concurrent physiological changes in chromophore concentration as described in a previous study.

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)0.000
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 score0.895
Threshold uncertainty score0.478

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 head0.010
GPT teacher head0.368
Teacher spread0.358 · 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

Citations1
Published2017
Admission routes2
Has abstractyes

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