Abstract

The differential diagnosis for optic atrophy can be challenging and requires expensive, time-consuming ancillary testing to determine the cause. While Leber's hereditary optic neuropathy (LHON) and optic neuritis (ON) are both clinically significant causes for optic atrophy, both relatively rare in the general population, contributing to limitations in obtaining large imaging datasets. This study therefore aims to develop a deep learning (DL) model based on small datasets that could distinguish the cause of optic disc atrophy using only fundus photography. We retrospectively reviewed fundus photographs of 120 normal eyes, 30 eyes (15 patients) with genetically-confirmed LHON, and 30 eyes (26 patients) with ON. Images were split into a training dataset and a test dataset and used for model training with ResNet-18. To visualize the critical regions in retinal photographs that are highly associated with disease prediction, Gradient-Weighted Class Activation Map (Grad-CAM) was used to generate image-level attention heat maps and to enhance the interpretability of the DL system. In the 3-class classification of normal, LHON, and ON, the area under the receiver operating characteristic curve (AUROC) was 1.0 for normal, 0.988 for LHON, and 0.990 for ON, clearly differentiating each class from the others with an overall total accuracy of 0.93. Specifically, when distinguishing between normal and disease cases, the precision, recall, and F1 scores were perfect at 1.0. Furthermore, in the differentiation of LHON from other conditions, ON from others, and between LHON and ON, we consistently observed precision, recall, and F1 scores of 0.8. The model performance was maintained until only 10% of the pixel values of the image, identified as important by Grad-CAM, were preserved and the rest were masked, followed by retraining and evaluation.

Details

Title
Development of a deep learning model to distinguish the cause of optic disc atrophy using retinal fundus photography
Author
Lee, Dong Kyu 1   VIAFID ORCID Logo  ; Choi, Young Jo 2   VIAFID ORCID Logo  ; Lee, Seung Jae 1   VIAFID ORCID Logo  ; Kang, Hyun Goo 1   VIAFID ORCID Logo  ; Park, Yu Rang 2   VIAFID ORCID Logo 

 Yonsei University College of Medicine, Department of Ophthalmology, Institute of Vision Research, Severance Eye Hospital, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454) 
 Yonsei University College of Medicine, Department of Biomedical Systems Informatics, Seoul, Republic of Korea (GRID:grid.15444.30) (ISNI:0000 0004 0470 5454) 
Pages
5079
Publication year
2024
Publication date
2024
Publisher
Nature Publishing Group
e-ISSN
20452322
Source type
Scholarly Journal
Language of publication
English
ProQuest document ID
2933663943
Copyright
© The Author(s) 2024. This work is published under http://creativecommons.org/licenses/by/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.