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 \(\pm 0.5D\)), 86.85% (between \(\pm 1D\)), 95.54% (between \(\pm 1.5D\)), and 98.57% (between \(\pm 2D\)), 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 \(\pm 0.5D\)), 3.76% (between \(\pm 1D\)), 6.15% (between \(\pm 1.5D\)), and 8.78% (between \(\pm 2D\)). 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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Acknowledgments
This work was supported in part by Guangdong Provincial Department of Education (2020ZDZX3043), Guangdong Provincial Key Laboratory (2020B121201001), and Shenzhen Natural Science Fund (JCYJ20200109140820699 and the Stable Support Plan Program 20200925174052004).
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Zhang, Y. et al. (2021). Juvenile Refractive Power Prediction Based on Corneal Curvature and Axial Length via a Domain Knowledge Embedding Network. In: Fu, H., Garvin, M.K., MacGillivray, T., Xu, Y., Zheng, Y. (eds) Ophthalmic Medical Image Analysis. OMIA 2021. Lecture Notes in Computer Science(), vol 12970. Springer, Cham. https://doi.org/10.1007/978-3-030-87000-3_10
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DOI: https://doi.org/10.1007/978-3-030-87000-3_10
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