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Record W2537322208 · doi:10.1109/embc.2016.7592097

Wearable diagnostic system for age-related macular degeneration

2016· article· en· W2537322208 on OpenAlexafffund
Navid Mohaghegh, Ebrahim Ghafar Zadeh, Sebastian Magierowski

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicRetinal Diseases and Treatments
Canadian institutionsYork University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsMacular degenerationWearable computerComputer scienceGraphical user interfaceInterface (matter)SoftwareGridComputer hardwareEmbedded systemComputer visionMedicineOphthalmologyOperating system

Abstract

fetched live from OpenAlex

This paper presents a novel head-mounted point-of-care diagnostic system for detection and continuous monitoring of Age-related Macular Degeneration (AMD). This wearable embedded open-source platform enables accurate monitoring of AMD by taking advantage of multiple standard graphical interface techniques such as Amsler Grid, Threshold Amsler Grid, Macular Computerized Psychophysical Test and Preferential Hyperacuity Perimeter (PHP). Here, we describe the proposed multi-Grid or so-called NGRID software and elaborate on the hardware prototype. This prototype includes a commercially available Oculus HMD incorporated with a single board computer. As the first step towards a fully integrated wearable system, this paper successfully proves the functionality of head-mounted graphical interface device ready for a live demonstration. Participants can experience this device and take a 10-minute AMD eye-exam. Furthermore, NGRID has been approved and permitted for an in-hospital clinical trial.

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)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.020

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0010.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0010.000
Insufficient payload (model declined to judge)0.0060.002

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 head0.013
GPT teacher head0.259
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

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 designBench or experimental
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

Citations6
Published2016
Admission routes2
Has abstractyes

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