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Record W3094917983 · doi:10.1364/boe.403209

Quantitative multi-contrast in vivo mouse imaging with polarization diversity optical coherence tomography and angiography

2020· article· en· W3094917983 on OpenAlexafffund
Destiny Hsu, Ji Hoon Kwon, Ringo Ng, Shuichi Makita, Yoshiaki Yasuno, Marinko V. Šarunic, Myeong Jin Ju

Bibliographic record

VenueBiomedical Optics Express · 2020
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsUniversity of British ColumbiaSimon Fraser University
FundersNatural Sciences and Engineering Research Council of CanadaCanadian Institutes of Health Research
KeywordsRetinal pigment epitheliumRetinaRetinalOptical coherence tomographyOptical coherence tomography angiographyMacular degenerationContrast (vision)AngiographyIn vivoOphthalmologyMedicinePathologyBiologyComputer scienceArtificial intelligenceRadiologyNeuroscience

Abstract

fetched live from OpenAlex

Retinal microvasculature and the retinal pigment epithelium (RPE) play vital roles in maintaining the health and metabolic activity of the eye. Visualization of these retina structures is essential for pre-clinical studies of vision-robbing diseases, such as age-related macular degeneration (AMD). We have developed a quantitative multi-contrast polarization diversity OCT and angiography (QMC-PD-OCTA) system for imaging and visualizing pigment in the RPE using degree of polarization uniformity (DOPU), along with flow in the retinal capillaries using OCT angiography (OCTA). An adaptive DOPU averaging kernel was developed to increase quantifiable values from visual data, and QMC en face images permit simultaneous visualization of vessel location, depth, melanin region thickness, and mean DOPU values, allowing rapid identification and differentiation of disease symptoms. The retina of five different mice strains were measured in vivo , with results demonstrating potential for pre-clinical studies of retinal disorders.

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)0.000
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: Empirical
Teacher disagreement score0.850
Threshold uncertainty score0.896

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.016
GPT teacher head0.227
Teacher spread0.211 · 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

Citations17
Published2020
Admission routes2
Has abstractyes

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