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Record W3084922366 · doi:10.1364/ol.410171

Label-free, non-contact, in vivo ophthalmic imaging using photoacoustic remote sensing microscopy

2020· article· en· W3084922366 on OpenAlexafffund
Zohreh Hosseinaee, Layla Khalili, James A. Tummon Simmons, Kevan Bell, Parsin Haji Reza

Bibliographic record

VenueOptics Letters · 2020
Typearticle
Languageen
FieldEngineering
TopicPhotoacoustic and Ultrasonic Imaging
Canadian institutionsIllumisonics (Canada)University of Waterloo
FundersCentre for Bioengineering and Biotechnology, University of WaterlooMitacsUniversity of WaterlooillumiSonicsCanada Foundation for InnovationNatural Sciences and Engineering Research Council of Canada
KeywordsScleraIn vivoPhotoacoustic imaging in biomedicineIRIS (biosensor)MicroscopyBiomedical engineeringPreclinical imagingMedicineRetinaMaterials sciencePathologyOphthalmologyOpticsBiologyComputer scienceNeuroscienceComputer vision

Abstract

fetched live from OpenAlex

We present, to the best of our knowledge, the first label-free, non-contact, in vivo imaging of the ocular vasculature using photoacoustic remote sensing (PARS) microscopy. Both anterior and posterior segments of a mouse eye were imaged. Vasculature of the iris, sclera, and retina tissues were clearly resolved. To the best of our knowledge, this is the first study showing non-contact photoacoustic imaging conducted on in vivo ocular tissue. We believe that PARS microscopy has the potential to advance the diagnosis and treatment of ocular diseases.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.862
Threshold uncertainty score1.000

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 integrity0.0000.001
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 head0.011
GPT teacher head0.227
Teacher spread0.215 · 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.

Study designBench or experimental
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

Citations22
Published2020
Admission routes2
Has abstractyes

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