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Record W4413379811 · doi:10.3390/photonics12080815

3D Printed Galilean Telescope for Low-Vision Patients

2025· article· en· W4413379811 on OpenAlexfundno aff
Daniel Aguirre-Aguirre, Itzel Muñoz-Juárez, M. I. Rodríguez-Rodríguez, Brenda Villalobos-Mendoza, Ruth Eva Hernández-Carbajal, Rufino Díaz-Uribe, Rafael Izazaga-Pérez

Bibliographic record

VenuePhotonics · 2025
Typearticle
Languageen
FieldMedicine
TopicOphthalmology and Visual Impairment Studies
Canadian institutionsnot available
FundersDirección General de Asuntos del Personal Académico, Universidad Nacional Autónoma de MéxicoCanadian Institute for Theoretical Astrophysics
KeywordsGalilean moonsGalileanOpticsTelescope3d printedPhysicsComputer scienceAstronomyRemote sensingOptoelectronicsGeologySatelliteEngineering

Abstract

fetched live from OpenAlex

Low vision is a condition in which a person experiences a significant loss of visual acuity or visual field that ordinary glasses, surgery, or medication cannot correct. Individuals suffering from this condition struggle to perform daily tasks, even when using glasses or contact lenses. In some cases, telescopes are recommended for patients with low vision diagnosis because they could help them improve their quality of life. Therefore, we propose a 3D-printed Galilean telescope for low-vision patients, accessible to both the vulnerable and nonvulnerable sectors of the population, with the advantages that the fabrication time, cost, and weight are considerably reduced. The performance of the 3D-printed Galilean telescope was evaluated by comparing it to an identical N-BK7 glass Galilean telescope design, obtaining a difference of 0.49 lp/mm in optical resolution. Clinical results from a patient with low vision, obtained as part of a proof-of-concept study, showed that the 3D-printed Galilean telescope improved the patient’s visual acuity, increasing it by up to 4 lines on the LEA numbers, from 10/80 to 10/32. Additionally, the 3D telescope enhanced the patient’s contrast sensitivity, improving it from 6 cpd (cycles per degree) level 8 to 18 cpd level 4.

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 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)0.000
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 score0.065
Threshold uncertainty score0.362

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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)0.0000.000

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 head0.017
GPT teacher head0.361
Teacher spread0.344 · 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 teacher head, 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

Citations0
Published2025
Admission routes1
Has abstractyes

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