TY - GEN
T1 - Juvenile Refractive Power Prediction Based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network
AU - Zhang, Yang
AU - Higashita, Risa
AU - Xu, Yanwu
AU - Santo, Daisuke
AU - Hu, Yan
AU - Long, Guodong
AU - Liu, Jiang
N1 - Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
PY - 2021
Y1 - 2021
N2 - 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.
AB - 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.
KW - Neural network
KW - Non-cycloplegic detection
KW - Refractive power prediction
UR - http://www.scopus.com/inward/record.url?scp=85115861198&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-87000-3_10
DO - 10.1007/978-3-030-87000-3_10
M3 - Conference contribution
AN - SCOPUS:85115861198
SN - 9783030869991
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 92
EP - 100
BT - Ophthalmic Medical Image Analysis - 8th International Workshop, OMIA 2021, Held in Conjunction with MICCAI 2021, Proceedings
A2 - Fu, Huazhu
A2 - Garvin, Mona K.
A2 - MacGillivray, Tom
A2 - Xu, Yanwu
A2 - Zheng, Yalin
PB - Springer Science and Business Media Deutschland GmbH
T2 - 8th 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
Y2 - 27 September 2021 through 27 September 2021
ER -