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Comparison of different smartphone cameras to evaluate conjunctival hyperaemia in normal subjects

journal contribution
posted on 2023-08-30, 16:22 authored by Carles Otero, Nery García-Porta, Juan Tabernero, Shahina Pardhan
Despite the significant advantages that smartphones’ cameras can provide in teleophthalmology and artificial intelligence applications, their use as black-box systems for clinical data acquisition, without adequate information of the quality of photographs can compromise data accuracy. The aim of this study is to compare the objective and subjective quantification of conjunctival redness in images obtained with calibrated and non-calibrated cameras, in different lighting conditions and optical magnifications. One hundred ninety-two pictures of the eye were taken in 4 subjects using 3 smartphone cameras{Bq, Iphone, Nexus}, 2 lighting levels{high 815 lx, low 122 lx} and 2 magnification levels{high 10x, low 6x}. Images were duplicated: one set was white balanced and color corrected (calibrated) and the other was left as it was. Each image was subjective and objectively evaluated. There were no significant differences in subjective evaluation in any of the conditions whereas many statistically significant main effects and interaction effects were shown for all the objective metrics. The clinician’s evaluation was not affected by different cameras, lighting conditions or optical magnifications, demonstrating the effectiveness of the human eye’s color constancy properties. However, calibration of a smartphone’s camera is essential when extracting objective data from images.

History

Refereed

  • Yes

Volume

9

Issue number

1

Page range

1339

Publication title

Scientific Reports

ISSN

2045-2322

Publisher

Nature Research

File version

  • Accepted version

Language

  • eng

Legacy posted date

2019-07-02

Legacy creation date

2019-07-02

Legacy Faculty/School/Department

Faculty of Health, Education, Medicine & Social Care

Note

This research was supported by BEVISION. This project has received funding from the European Union’s Horizon 2020 research and innovation programme under the Marie Skłodowska-Curie grant agreement No 747441

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