Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera

Abstract Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study,...

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Main Authors:	Yong Chan Kim, Dong Jin Chang, So Jin Park, In Young Choi, Ye Seul Gong, Hyun-Ah Kim, Hyung Bin Hwang, Kyung In Jung, Hae-young Lopilly Park, Chan Kee Park, Kui Dong Kang
Format:	Article
Language:	English
Published:	Nature Publishing Group 2021-03-01
Series:	Scientific Reports
Online Access:	https://doi.org/10.1038/s41598-021-85699-0

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spelling	doaj-e93f5d221c9a40ccb515c85365b2fafc2021-03-28T11:31:08ZengNature Publishing GroupScientific Reports2045-23222021-03-0111111310.1038/s41598-021-85699-0Machine learning prediction of pathologic myopia using tomographic elevation of the posterior scleraYong Chan Kim0Dong Jin Chang1So Jin Park2In Young Choi3Ye Seul Gong4Hyun-Ah Kim5Hyung Bin Hwang6Kyung In Jung7Hae-young Lopilly Park8Chan Kee Park9Kui Dong Kang10Department of Ophthalmology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of KoreaDepartment of Medical Informatics, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Seoul St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaDepartment of Ophthalmology, Incheon St. Mary’s Hospital, College of Medicine, The Catholic University of KoreaAbstract Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.https://doi.org/10.1038/s41598-021-85699-0
collection	DOAJ
language	English
format	Article
sources	DOAJ
author	Yong Chan Kim Dong Jin Chang So Jin Park In Young Choi Ye Seul Gong Hyun-Ah Kim Hyung Bin Hwang Kyung In Jung Hae-young Lopilly Park Chan Kee Park Kui Dong Kang
spellingShingle	Yong Chan Kim Dong Jin Chang So Jin Park In Young Choi Ye Seul Gong Hyun-Ah Kim Hyung Bin Hwang Kyung In Jung Hae-young Lopilly Park Chan Kee Park Kui Dong Kang Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera Scientific Reports
author_facet	Yong Chan Kim Dong Jin Chang So Jin Park In Young Choi Ye Seul Gong Hyun-Ah Kim Hyung Bin Hwang Kyung In Jung Hae-young Lopilly Park Chan Kee Park Kui Dong Kang
author_sort	Yong Chan Kim
title	Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
title_short	Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
title_full	Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
title_fullStr	Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
title_full_unstemmed	Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
title_sort	machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera
publisher	Nature Publishing Group
series	Scientific Reports
issn	2045-2322
publishDate	2021-03-01
description	Abstract Qualitative analysis of fundus photographs enables straightforward pattern recognition of advanced pathologic myopia. However, it has limitations in defining the classification of the degree or extent of early disease, such that it may be biased by subjective interpretation. In this study, we used the fovea, optic disc, and deepest point of the eye (DPE) as the three major markers (i.e., key indicators) of the posterior globe to quantify the relative tomographic elevation of the posterior sclera (TEPS). Using this quantitative index from eyes of 860 myopic patients, support vector machine based machine learning classifier predicted pathologic myopia an AUROC of 0.828, with 77.5% sensitivity and 88.07% specificity. Axial length and choroidal thickness, the existing quantitative indicator of pathologic myopia only reached an AUROC of 0.758, with 75.0% sensitivity and 76.61% specificity. When all six indices were applied (four TEPS, AxL, and SCT), the discriminative ability of the SVM model was excellent, demonstrating an AUROC of 0.868, with 80.0% sensitivity and 93.58% specificity. Our model provides an accurate modality for identification of patients with pathologic myopia and may help prioritize these patients for further treatment.
url	https://doi.org/10.1038/s41598-021-85699-0
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Machine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera

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