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Record W2021513243 · doi:10.1364/josaa.29.002598

Multimodal adaptive optics retinal imager: design and performance

2012· article· en· W2021513243 on OpenAlexfundno aff
Daniel X. Hammer, R. Daniel Ferguson, Mircea Mujat, Ankit Patel, Emily Plumb, Nicusor Iftimia, Toco Yuen Ping Chui, James D. Akula, Anne B. Fulton

Bibliographic record

VenueJournal of the Optical Society of America A · 2012
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersNational Center for Research ResourcesNational Institutes of HealthNational Eye InstituteUniversity of Waterloo
KeywordsAdaptive opticsScanning laser ophthalmoscopyOptical coherence tomographyComputer scienceOpticsWavefrontComputer visionArtificial intelligencePhysics

Abstract

fetched live from OpenAlex

Optical coherence tomography (OCT) and scanning laser ophthalmoscopy (SLO) are complementary imaging modalities, the combination of which can provide clinicians with a wealth of information to detect retinal diseases, monitor disease progression, or assess new therapies. Adaptive optics (AO) is a tool that enables correction of wavefront distortions from ocular aberrations. We have developed a multimodal adaptive optics system (MAOS) for high-resolution multifunctional use in a variety of research and clinical applications. The system integrates both OCT and SLO imaging channels into an AO beam path. The optics and hardware were designed with specific features for simultaneous SLO/OCT output, for high-fidelity AO correction, for use in humans, primates, and small animals, and for efficient location and orientation of retinal regions of interest. The MAOS system was tested on human subjects and rodents. The design, performance characterization, and initial representative results from the human and animal studies are presented and discussed.

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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.198
Threshold uncertainty score0.277

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 studies0.0000.001
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 head0.035
GPT teacher head0.321
Teacher spread0.286 · 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

Citations58
Published2012
Admission routes1
Has abstractyes

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