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Record W2086239775 · doi:10.1117/12.531311

Simultaneous OCT/ICG fluorescence imaging system for investigations of the ocular fundus

2004· article· en· W2086239775 on OpenAlexaff
George Dobre, R. Cernat, Adrian Podoleanu, Justin Pedro, Richard B. Rosen

Bibliographic record

VenueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIE · 2004
Typearticle
Languageen
FieldMedicine
TopicRetinal and Macular Surgery
Canadian institutionsNanoacademic Technologies
FundersEngineering and Physical Sciences Research CouncilResearch Councils UKNew York Eye and Ear Infirmary of Mount Sinai
KeywordsOptical coherence tomographyIndocyanine greenConfocalFundus (uterus)FluorescenceMaterials scienceOpticsConfocal microscopyChinFluorescence-lifetime imaging microscopyBiomedical engineeringMedicineOphthalmologyAnatomyPhysics

Abstract

fetched live from OpenAlex

The authors report the construction of a dual channel OCT/indocyanine green (ICG) fluorescence system with the optical source common to both the OCT and fluorescence channels based on our previously described ophthalmic Optical Coherence Tomography (OCT)/confocal imaging system. The confocal channel is tuned to the fluorescence wavelength range of ICG dye. The system is compact and assembled on a chin rest and it enables the clinician to visualise the same area of the eye fundus in terms of both en face OCT slices and ICG angiograms, displayed side by side. The images are collected by fast en-face scanning (C-scan) followed by slower scanning along a transverse direction and depth scanning. We demonstrate the first such dual OCT/ICG-fluorescence images from healthy eyes. The system is still capable of providing chosen OCT B-scans at selected points from the ICG confocal image, in the same way the OCT/confocal configuration was used.

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)0.001
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: Methods · Consensus signal: Methods
Teacher disagreement score0.003
Threshold uncertainty score0.010

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.001
Open science0.0010.001
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0030.001

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 head0.011
GPT teacher head0.228
Teacher spread0.217 · 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
GenreMethods

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
Published2004
Admission routes1
Has abstractyes

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Same venueProceedings of SPIE, the International Society for Optical Engineering/Proceedings of SPIESame topicRetinal and Macular SurgeryFrench-language works237,207