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Record W3135194109 · doi:10.1117/12.2578657

Is the optic disc tilt angle different in myopia?

2021· article· en· W3135194109 on OpenAlexaff
Nitish Gudapati, S. Swedha, Vasudevan Lakshminarayanan

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicGlaucoma and retinal disorders
Canadian institutionsUniversity of Waterloo
Fundersnot available
KeywordsEmmetropiaArtificial intelligenceOptical coherence tomographyGrayscaleThresholdingDilation (metric space)Adaptive histogram equalizationOptic discOptic nerveComputer visionComputer scienceMathematicsGlaucomaPixelHistogram equalizationHistogramRefractive errorOphthalmologyMedicineEye diseaseImage (mathematics)

Abstract

fetched live from OpenAlex

Optic disc tilt (ODT), peripapillary atrophy (PPA), and abnormally large or small optic discs are the earliest known changes in myopic eyes and may precede the development of pathological myopia. Increasing ODT and distance between the macula and optic nerve head have been reported as being associated with progressive myopia. Therefore, it is important to segment and quantify the ODT accurately. Using a newly developed automated image processing method we measured the ODT in both myopes and emmetropes. We determined the ODT from myopic eyes (n= 90) and compared the results with emmetropia (n=14). All 104 optical coherence tomography (OCT) images had dimensions of 200x200 pixels corresponding to 6mm x 6mm. The myopic OCT images were labeled based on a severity scale based on the spherical error (SE) as low (-0.5 to -3.00 D SE), moderate (-3.12 to -6.00 D SE), high (-6.12 to -9.00 D SE), and very high (worse than -9.00 D SE) using standard myopia classifications. Each OCT image was segmented by a clinician (CEM) and by the newly proposed method (NAM). The NAM used 8-bit grayscale OCT images which was preprocessed by applying Gaussian blur and Contrast Limited Adaptive Histogram Equalization based thresholding to remove noise and locate regions of interest. Then the images were split into two halves to fit straight lines separately. Morphological erosion and dilation were performed on the images to remove artifacts. They were tested with three combinations of erosion and dilation iterations. Univariate linear regression Lines were fit to trace the white band on each half and angle between the lines was determined. Both the methods showed higher horizontal ODT than vertical. The mean ± SD horizontal ODT (in degrees) in the myopic eye was 18.47 ± 7.67 and 15.84± 6.61 by the NAM and CEM methods. The vertical ODT in the myopic eyes (in degrees) was 16.32 ± 7.10, and 14.52 ± 7.05 by the NAM and CEM methods respectively. However, the NAM showed a maximum difference (2.26 ± 5.68) between horizontal and vertical ODT. The study results show that the ODT in very high myopic eyes (26.33±8.99)is significantly different (p<0.05) when compared to emmetropic eyes (19.47±3.99).

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)0.003
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 score0.003
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.014
GPT teacher head0.263
Teacher spread0.249 · 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

Citations2
Published2021
Admission routes1
Has abstractyes

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