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Record W3163960888 · doi:10.1016/j.xops.2021.100030

Quantitative Parameters from OCT Angiography in Patients with Diabetic Retinopathy and in Those with Only Peripheral Retinopathy Compared with Control Participants

2021· article· en· W3163960888 on OpenAlexfundno aff
Ruth Hogg, David M. Wright, Rosa Dolz‐Marco, Calum Gray, Nadia K. Waheed, Michel M. Teussink, Timos Naskas, Jennifer Perais, Radha Das, Nicola Quinn, Georgios Bontzos, Constantinos Nicolaou, Kaushik Annam, Ian Young, Frank Kee, Bernadette McGuiness, Gareth Mc Kay, Tom MacGillivray, Tünde Pető, Usha Chakravarthy

Bibliographic record

VenueOphthalmology Science · 2021
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsnot available
FundersNational Institute on AgingHealth and Social Care Research and Development DivisionEconomic and Social Research CouncilOffice of the First Minister and Deputy First MinisterQueen's UniversityCarl Zeiss Meditec AGHeidelberg EngineeringApellis PharmaceuticalsQueen's University BelfastDiabetes UKCollege of OptometristsWellcome TrustAlzheimer's SocietyCentre for Ageing Research and Development in IrelandUnited Kingdom Clinical Research CollaborationRegeneron PharmaceuticalsPublic Health AgencyMacular SocietyMedical Research CouncilAtlantic Philanthropies
KeywordsMedicineDiabetic retinopathyOphthalmologyPerimeterFundus photographyFundus (uterus)RetinalDiabetes mellitusPlexusNerve fiber layerPopulationRetinopathyOptometryFluorescein angiographySurgeryMathematics

Abstract

fetched live from OpenAlex

Purpose: To describe the differences in a range of quantitative OCT angiography (OCTA) metrics across early stages of diabetic retinopathy (DR), providing robust effect estimates as well as sensitivity and specificity. Design: Cross-sectional study with population-based sampling. Participants: Four hundred forty-one eyes from 296 individuals: 328 control eyes (no diabetes mellitus [DM] and no DR), 55 eyes with DM and no DR, and 58 eyes with early nonproliferative DR. Methods: Multimodal retinal imaging included color fundus photography, color Optomap ultra-widefield imaging, and spectral-domain OCT (Spectralis OCT2; Heidelberg Engineering GmbH) with the OCTA module. All images were graded for the presence and severity of DR features. OCTA images were assessed manually for inclusion based on quality. Binary OCTA metrics were assessed after 3-dimensional projection artifact removal including from the nerve fiber layer vascular plexus, superficial vascular plexus (SVC), and deep vascular plexus (DVC) by Early Treatment Diabetic Retinopathy Study (ETDRS) grid, foveal avascular zone (FAZ) area, FAZ minimum and maximum diameter, perimeter length, and circularity. Main Outcome Measures: Diabetes mellitus and DR status and presence or absence of DR in the retinal periphery. Results: The reduction in vessel densities in participants with DM and manifest DR compared with control participants tended to be twice that of those with DM, but no DR, compared with control participants. Some evidence of spatial heterogeneity in vessel reductions was found in those yet to develop DR, whereas those with manifest DR had significant reductions across the ETDRS grid. The FAZ perimeter and circularity were impacted most significantly by DM, and those with DR showed decreased multispectral fractal dimensions compared with control participants. Eyes with peripheral DR had reduced vessel density compared with those with DM and no DR only in the superior outer, temporal inner, and temporal outer regions in the DVC and SVC. The area under the receiver operating characteristic curve ranged between 0.48 and 0.73. Conclusions: Significant differences in OCTA metrics can be found in those with DM before manifest DR using commercially available equipment with minimal image postprocessing. Although diagnostic performance was poor, these metrics may be useful for measuring change over time in clinical trials.

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)0.006
Version: metacan-v3-hybrid-931329e0061cValidation 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 score0.001
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.021
GPT teacher head0.287
Teacher spread0.266 · 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 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
Published2021
Admission routes1
Has abstractyes

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