| NO F-004 |
| Accuracy of Machine Learning for Differentiation between Optic Neuropathies and Pseudopapilledema |
| 1: Department of Bioinformatics and Life Science, Soongsil University, Seoul, Korea.
2: Functional Genome Institute, PDXen Biosystems Inc., Seoul, Republic of Korea.
3: Kim’s Eye Hospital, Seoul, Korea
4: Department of Ophthalmology, Konyang University College of Medicine, Seoul, Korea |
| Jin Mo Ahn,1 Sangsoo Kim,1 Kwang-Sung Ahn,2 Sung-Hoon Cho2, Ungsoo S. Kim3,4 |
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목적 : To evaluate the accuracy of machine learning for differentiation between optic neuropathies and pseudopapilledema (PPE).
방법 : Two hundred and ninety-five images of optic neuropathies, 295 images of PPE, and 779 control images. Pseudopapilledema was defined as follows: cases with elevated optic nerve head and blurred disc margin, with normal visual acuity (>0.8 Snellen visual acuity), visual field, color vision, and pupillary reflex. We compared four machine learning classifiers (our model, GoogleNet Inception v3, 19-layer Very Deep Convolution Network from Visual Geometry group (VGG), and 50-layer Deep Residual Learning (ResNet)).
결과 : The accuracy of machine learning classifiers ranged from 95.89% to 98.63% (our model: 95.89%, Inception V3: 96.45%, ResNet: 98.63%, and VGG: 96.80%). A high AUROC score was noted in both ResNet and VGG (0.999).
결론 : Machine learning techniques can be combined with fundus photography as an effective approach to distinguish between PPE and elevated optic disc associated with optic neuropathies.
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