Juvenile Refractive Power Prediction Based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network

Yang Zhang, Risa Higashita, Yanwu Xu, Daisuke Santo, Yan Hu, Guodong Long, Jiang Liu

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

Abstract

Traditional cycloplegic refractive power detection with specific lotions dropping may cause side-effects, e.g., the pupillary retraction disorder, on juvenile eyes. In this paper, we develop a novel neural network algorithm to predict the refractive power, which is assessed by the Spherical Equivalent (SE), using real-world clinical non-cycloplegic refraction records. Participants underwent a comprehensive ophthalmic examination to obtain several related parameters, including sphere degree, cylinder degree, axial length, flat keratometry, and steep keratometry. Based on these quantitative biomedical parameters, a novel neural network model is trained to predict the SE. On the whole age test dataset, the domain knowledge embedding network (DKE-Net) prediction accuracies of SE achieve 59.82% (between ± 0.5 D ), 86.85% (between ± 1 D ), 95.54% (between ± 1.5 D ), and 98.57% (between ± 2 D ), which demonstrate superior performance over conventional machine learning algorithms on real-world clinical electronic refraction records. Also, the SE prediction accuracies on the excluded examples that are disqualified for model training, are 2.16% (between ± 0.5 D ), 3.76% (between ± 1 D ), 6.15% (between ± 1.5 D ), and 8.78% (between ± 2 D ). This is the leading application to predict refraction power using a neural network and domain knowledge, to the best of our knowledge, with a satisfactory accuracy level. Moreover, the model can also assist in diagnosing some specific kinds of ocular disorders.

Original languageEnglish
Title of host publicationOphthalmic Medical Image Analysis - 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Proceedings
EditorsHuazhu Fu, Mona K. Garvin, Tom MacGillivray, Yanwu Xu, Yalin Zheng
PublisherSpringer Science and Business Media Deutschland GmbH
Pages92-100
Number of pages9
ISBN (Print)9783030869991
DOIs
Publication statusPublished - 2021
Externally publishedYes
Event8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021 - Virtual, Online
Duration: 27 Sep 202127 Sep 2021

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12970 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference8th International Workshop on Ophthalmic Medical Image Analysis, OMIA 2021 held in conjunction with 24th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2021
CityVirtual, Online
Period27/09/2127/09/21

Keywords

  • Neural network
  • Non-cycloplegic detection
  • Refractive power prediction

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science (all)

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