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Proceedings Paper

Accessing refractive errors via eccentric infrared photorefraction based on deep learning
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Paper 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.

Paper Details

Date Published: 12 November 2019
PDF: 3 pages
Proc. SPIE 11197, SPIE Future Sensing Technologies, 111970S (12 November 2019); doi: 10.1117/12.2542652
Show Author Affiliations
Chia-Chi Yang, National Chiao Tung Univ. (Taiwan)
Jian-Jia Su, National Chiao Tung Univ. (Taiwan)
Jie-En Li, National Chiao Tung Univ. (Taiwan)
Zhi-Yu Zhu, National Chiao Tung Univ. (Taiwan)
Jin-Shing Tseng, Medimaging Integrated Solution Inc. (Taiwan)
Chu-Ming Cheng, Medimaging Integrated Solution Inc. (Taiwan)
Chung-Hao Tien, National Chiao Tung Univ. (Taiwan)

Published in SPIE Proceedings Vol. 11197:
SPIE Future Sensing Technologies
Masafumi Kimata; Christopher R. Valenta, Editor(s)

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