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

Zhang, Y; Higashita, R; Xu, Y; Santo, D; Hu, Y; Long, G; Liu, J

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

Zhang, Y Higashita, R Xu, Y Santo, D Hu, Y Long, G

Liu, J

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Publisher:: Springer
Publication Type:: Conference Proceeding
Citation:: Ophthalmic Medical Image Analysis, 2021, 12970 LNCS, pp. 92-100
Issue Date:: 2021-01-01

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Field	Value	Language
dc.contributor.author	Zhang, Y
dc.contributor.author	Higashita, R
dc.contributor.author	Xu, Y
dc.contributor.author	Santo, D
dc.contributor.author	Hu, Y
dc.contributor.author	Long, G https://orcid.org/0000-0003-3740-9515
dc.contributor.author	Liu, J
dc.date	2021-09-27
dc.date.accessioned	2022-06-10T19:43:08Z
dc.date.available	2022-06-10T19:43:08Z
dc.date.issued	2021-01-01
dc.identifier.citation	Ophthalmic Medical Image Analysis, 2021, 12970 LNCS, pp. 92-100
dc.identifier.isbn	9783030869991
dc.identifier.issn	0302-9743
dc.identifier.issn	1611-3349
dc.identifier.uri	http://hdl.handle.net/10453/158076
dc.description.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.
dc.language	en
dc.publisher	Springer
dc.relation.ispartof	Ophthalmic Medical Image Analysis
dc.relation.ispartof	International Workshop on Ophthalmic Medical Image Analysis
dc.relation.ispartofseries	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
dc.relation.isbasedon	10.1007/978-3-030-87000-3_10
dc.rights	info:eu-repo/semantics/closedAccess
dc.subject.classification	Artificial Intelligence & Image Processing
dc.title	Juvenile Refractive Power Prediction Based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network
dc.type	Conference Proceeding
utslib.citation.volume	12970 LNCS
utslib.location.activity	Strasbourg, France
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Strength - AAII - Australian Artificial Intelligence Institute
utslib.copyright.status	closed_access	*
pubs.consider-herdc	false
dc.date.updated	2022-06-10T19:43:07Z
pubs.finish-date	2021-09-27
pubs.place-of-publication	Switzerland
pubs.publication-status	Published
pubs.start-date	2021-09-27
pubs.volume	12970 LNCS
dc.location	Switzerland

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.

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http://hdl.handle.net/10453/158076