Accessing refractive errors via eccentric infrared photorefraction based on deep learning

Chia Chi Yang, Jian Jia Su, Jie En Li, Zhi Yu Zhu, Jin Shing Tseng, Chu Ming Cheng, Chung Hao Tien*

*Corresponding author for this work

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

Abstract

Eccentric infrared photorefraction is an attractive vision screening method which is widely used for uncooperative subjects, such as infants and toddlers. Unlike conventional slope-based photorefraction, a deep neural network is used to predict refractive error in this study. Total 1216 ocular image were collected by a homemade photorefraction device, whose corresponding refractive error was measured by a commercial autorefractor device, to create a series of dataset for our deep neural network. The mean squared error of the preliminary result is ±0.9 diopter, which indicates its feasibility and can be improved with bigger database.

Original languageEnglish
Title of host publicationSPIE Future Sensing Technologies
EditorsMasafumi Kimata, Christopher R. Valenta
PublisherSPIE
ISBN (Electronic)9781510631113
DOIs
StatePublished - 1 Jan 2019
EventSPIE Future Sensing Technologies 2019 - Tokyo, Japan
Duration: 14 Nov 2019 → …

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume11197
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

ConferenceSPIE Future Sensing Technologies 2019
Country/TerritoryJapan
CityTokyo
Period14/11/19 → …

Keywords

  • Deep learning
  • Digital imaging processing
  • Optical system
  • Photorefraction
  • Refractive error

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