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Record W2206431756 · doi:10.1109/imctl.2015.7359594

AR stereoscopic 3D Human Eye Examination App

2015· article· en· W2206431756 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Health Research
Canadian institutionsOntario Tech University
Fundersnot available
KeywordsStereoscopyComputer scienceComputer visionComputer graphics (images)Artificial intelligence

Abstract

fetched live from OpenAlex

The fundus eye exam, an important ophthalmologic assessment procedure that allows examining the eye's health, is taught by demonstration and guided practices whereby the trainees practice on each other and expertise is gained through experience using an ophthalmoscope. However, in addition to the issues associated with such an apprenticeship model, the anatomy of the eye's intricate oculomotor system is conceptually difficult for novice trainees to grasp. The examination is based on 2D eye fundus images that without proper training and skills abnormalities in the eye can be overlooked. Although virtual anatomy and simulators are available to alleviate some of these issues, these still require an elevated investment and infrastructure and are typically limited to one user at a time. Our ongoing work is seeing the development of an engaging and interactive stereoscopic augmented reality app. The app allows a student to navigate, in an immersive stereoscopic 3D environment, the inner volumetric shape of the eye important to detect features and pathologies.

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.

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)0.001
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 score0.061
Threshold uncertainty score0.842

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.308
GPT teacher head0.556
Teacher spread0.247 · 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

Quick stats

Citations10
Published2015
Admission routes1
Has abstractyes

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