Evidence map›Paper›PMID 38062067›Full record

ArticleScientific reports2023

Dynamic accommodation measurement using Purkinje reflections and machine learning.

Faik Ozan Ozhan, Ugur Aygun, Afsun Sahin, Hakan Urey

Abstract read
In one paragraph

Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Faik Ozan OzhanDepartment of Electrical and Electronics Engineering, Koç University, 34450, Istanbul, Turkey.
Ugur AygunDepartment of Electrical and Electronics Engineering, Koç University, 34450, Istanbul, Turkey.
Afsun SahinKoç University Translational Medicine Research Center (KUTTAM), 34450, Istanbul, Turkey.
Hakan UreyDepartment of Electrical and Electronics Engineering, Koç University, 34450, Istanbul, Turkey. hurey@ku.edu.tr.

Funding

HORIZON EUROPE European Innovation Council 101057672Türkiye Bilimsel ve Teknolojik Araştırma Kurumu 120C145Türkiye Bilimsel ve Teknolojik Araştırma Kurumu 2210/A
6 · The paper itself

Abstract

Quantifying eye movement is important for diagnosing various neurological and ocular diseases as well as AR/VR displays. We developed a simple setup for real-time dynamic gaze tracking and accommodation measurements based on Purkinje reflections, which are the reflections from front and back surfaces of the cornea and the eye lens. We used an accurate eye model in ZEMAX to simulate the Purkinje reflection positions at different focus distances of the eye, which matched the experimental data. A neural network was trained to simultaneously predict vergence and accommodation using data collected from 9 subjects. We demonstrated that the use of Purkinje reflection coordinates in machine learning resulted in precise estimation. The proposed system accurately predicted the accommodation with an accuracy better than 0.22 D using subject's own data and 0.40 D using other subjects' data with two-point calibration in tests performed with 9 subjects in our setup.

Indexed as

Accommodation, OcularLens, CrystallineCorneaEye MovementsHumansMachine Learning

Identifiers

PMID38062067
PMCPMC10703819

What OpenQuestion holds

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LicenceCC BY
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.