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Record W2132881881 · doi:10.1136/bjo.2008.146233

Colour versus grey-scale display of images on high-resolution spectral OCT

2009· article· en· W2132881881 on OpenAlexaboutno aff
Manjot Brar, D-U G Bartsch, N. Nigam, F. Mojana, Luis Gómez, Lingyun Cheng, Joshua Hedaya, William R. Freeman

Bibliographic record

VenueBritish Journal of Ophthalmology · 2009
Typearticle
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsnot available
FundersNational Eye InstituteUniversity of California, San Diego
KeywordsGrey scaleOptical coherence tomographyGrey levelRetinalMedicineVisibilityOphthalmologyGrading scaleScale (ratio)Artificial intelligenceRetinal pigment epitheliumEpiretinal membraneComputer visionOpticsComputer scienceImage (mathematics)CartographyVisual acuityVitrectomySurgeryPhysics

Abstract

fetched live from OpenAlex

AIM: To determine whether colour or grey-scale images from high-resolution spectral optical coherence tomography (OCT) are superior in visualising clinically important details of retinal structures. METHODS: Patients with macular pathologies were imaged using spectral OCT (OTI, Toronto, Canada). Two reviewers independently analysed the retinal structures and pathologies and graded them on a four-point scale on the basis of the visibility. A third reviewer masked to the results then reviewed images where there was a different score for colour versus grey scale. RESULTS: Statistical analysis showed the grey-scale image to be significantly better in visualising the details of epiretinal membrane, photoreceptor and retinal pigment epithelium layer morphology than the colour scale image (p = 0.00088-0.0006). In 16.17% of eyes, the colour image led to the false impression of photoreceptor disruption. CONCLUSION: Grey-scale images are qualitatively superior to the colour-scale images on high-resolution spectral OCT. Colour images can be misleading, as the displayed colours are false colours, and the observer may see a dramatic change in colour and interpret that as a large change in the OCT reflectivity.

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: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.912
Threshold uncertainty score0.538

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
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 head0.013
GPT teacher head0.255
Teacher spread0.243 · 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 designObservational
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

Citations11
Published2009
Admission routes1
Has abstractyes

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