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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2021
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oai:doaj.org-article:e93f5d221c9a40ccb515c85365b2fafc2021-12-02T17:04:35ZMachine learning prediction of pathologic myopia using tomographic elevation of the posterior sclera10.1038/s41598-021-85699-02045-2322https://doaj.org/article/e93f5d221c9a40ccb515c85365b2fafc2021-03-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-85699-0https://doaj.org/toc/2045-2322Abstract 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.Yong Chan KimDong Jin ChangSo Jin ParkIn Young ChoiYe Seul GongHyun-Ah KimHyung Bin HwangKyung In JungHae-young Lopilly ParkChan Kee ParkKui Dong KangNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-13 (2021) |
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Medicine R Science Q 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 |
| 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. |
| format |
article |
| 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 |
| 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 Portfolio |
| publishDate |
2021 |
| url |
https://doaj.org/article/e93f5d221c9a40ccb515c85365b2fafc |
| work_keys_str_mv |
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