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Record W3010730211 · doi:10.1097/icu.0000000000000653

The future of retinal imaging

2020· review· en· W3010730211 on OpenAlexaff
Daniel Q. Li, Netan Choudhry

Bibliographic record

VenueCurrent Opinion in Ophthalmology · 2020
Typereview
Languageen
FieldEngineering
TopicOptical Coherence Tomography Applications
Canadian institutionsMcGill University
Fundersnot available
KeywordsScanning laser ophthalmoscopyOphthalmoscopyRetinalRetinaOptical coherence tomographyAdaptive opticsFluorescein angiographyComputer scienceSoftware portabilityOptometryOpticsMedicineNeuroscienceOphthalmologyBiologyPhysics

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: This article reviews emerging technologies in retinal imaging, including their scientific background, clinical implications and future directions. RECENT FINDINGS: Fluorescence lifetime imaging ophthalmoscopy is a technology that will reveal biochemical and metabolic changes of the retina at the cellular level. Optical coherence tomography is evolving exponentially toward higher resolution, faster speed, increased portability and more cost effective. Adaptive optics scanning laser ophthalmoscopy fluorescein angiography will provide unprecedented detail of the retinal vasculature down to the level of capillaries, enabling earlier and more sensitive detection of retinal vascular diseases. SUMMARY: Continued developments in retinal imaging focus on improved resolution, faster speed and noninvasiveness, while providing new information on the structure-function relationship of the retina inclusive of metabolic activity at the cellular level.

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)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.007
Threshold uncertainty score0.023

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.001
Scholarly communication0.0020.003
Open science0.0010.001
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0070.006

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 head0.060
GPT teacher head0.378
Teacher spread0.318 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations18
Published2020
Admission routes1
Has abstractyes

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