Evidence map›Paper›PMID 32260312›Full record

ArticleSensors (Basel, Switzerland)2020

Spectral Filter Selection for Increasing Chromatic Diversity in CVD Subjects.

Miguel Ángel Martínez-Domingo, Eva M Valero, Luis Gómez-Robledo, Rafael Huertas, Javier Hernández-Andrés

Abstract read
In one paragraph

Article in Sensors (Basel, Switzerland), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.

0numbers the graph read from it
0cells of the map it votes in
4citing 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

4 citing papers in PubMed.

  1. Article
  2. Article
  3. Article
  4. 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

5 authors.

Miguel Ángel Martínez-DomingoDepartment of Optics, University of Granada, 18071 Granada, Spain.
Eva M ValeroDepartment of Optics, University of Granada, 18071 Granada, Spain.
Luis Gómez-RobledoDepartment of Optics, University of Granada, 18071 Granada, Spain.
Rafael HuertasDepartment of Optics, University of Granada, 18071 Granada, Spain.
Javier Hernández-AndrésDepartment of Optics, University of Granada, 18071 Granada, Spain.

Funding

Ministerio de Ciencia, Innovación y Universidades RTI2018-094738-B-I00Ministerio de Economía, Industria y Competitividad, Gobierno de España FIS2017-89258-P
6 · The paper itself

Abstract

This paper analyzes, through computational simulations, which spectral filters increase the number of discernible colors (NODC) of subjects with normal color vision, as well as red-green anomalous trichromats and dichromats. The filters are selected from a set of filters in which we have modeled spectral transmittances. With the selected filters we have carried out simulations performed using the spectral reflectances captured either by a hyperspectral camera or by a spectrometer. We have also studied the effects of these filters on color coordinates. Finally, we have simulated the results of two widely used color blindness tests: Ishihara and Farnsworth-Munsell 100 Hue (FM100). In these analyses the selected filters are compared with the commercial filters from EnChroma and VINO companies. The results show that the increase in NODC with the selected filters is not relevant. The simulation results show that none of these chosen filters help color vision deficiency (CVD) subjects to pass the set of color blindness tests studied. These results obtained using standard colorimetry support the hypothesis that the use of color filters does not cause CVDs to have a perception similar to that of a normal observer.

Indexed as

Color PerceptionColor Vision DefectsFiltrationHumansOptical DevicesOptics and Photonicsaids for the visually impairedcolor blindnesscolor imagingspectral imaging

Identifiers

PMID32260312
PMCPMC7180466

What OpenQuestion holds

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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.