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BMC Ophthalmology
Published in: BMC Ophthalmology 1/2020

Open Access 01-12-2020 | Research article

Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography

Authors: Baek Hwan Cho, Da Young Lee, Kyung-Ah Park, Sei Yeul Oh, Jong Hak Moon, Ga-In Lee, Hoon Noh, Joon Kyo Chung, Min Chae Kang, Myung Jin Chung

Published in: BMC Ophthalmology | Issue 1/2020

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Abstract

Background

It is necessary to consider myopic optic disc tilt as it seriously impacts normal ocular parameters. However, ophthalmologic measurements are within inter-observer variability and time-consuming to get. This study aimed to develop and evaluate deep learning models that automatically recognize a myopic tilted optic disc in fundus photography.

Methods

This study used 937 fundus photographs of patients with normal or myopic tilted disc, collected from Samsung Medical Center between April 2016 and December 2018. We developed an automated computer-aided recognition system for optic disc tilt on color fundus photographs via a deep learning algorithm. We preprocessed all images with two image resizing techniques. GoogleNet Inception-v3 architecture was implemented. The performances of the models were compared with the human examiner’s results. Activation map visualization was qualitatively analyzed using the generalized visualization technique based on gradient-weighted class activation mapping (Grad-CAM++).

Results

Nine hundred thirty-seven fundus images were collected and annotated from 509 subjects. In total, 397 images from eyes with tilted optic discs and 540 images from eyes with non-tilted optic discs were analyzed. We included both eye data of most included patients and analyzed them separately in this study. For comparison, we conducted training using two aspect ratios: the simple resized dataset and the original aspect ratio (AR) preserving dataset, and the impacts of the augmentations for both datasets were evaluated. The constructed deep learning models for myopic optic disc tilt achieved the best results when simple image-resizing and augmentation were used. The results were associated with an area under the receiver operating characteristic curve (AUC) of 0.978 ± 0.008, an accuracy of 0.960 ± 0.010, sensitivity of 0.937 ± 0.023, and specificity of 0.963 ± 0.015. The heatmaps revealed that the model could effectively identify the locations of the optic discs, the superior retinal vascular arcades, and the retinal maculae.

Conclusions

We developed an automated deep learning-based system to detect optic disc tilt. The model demonstrated excellent agreement with the previous clinical criteria, and the results are promising for developing future programs to adjust and identify the effect of optic disc tilt on ophthalmic measurements.
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Metadata
Title
Computer-aided recognition of myopic tilted optic disc using deep learning algorithms in fundus photography
Authors
Baek Hwan Cho
Da Young Lee
Kyung-Ah Park
Sei Yeul Oh
Jong Hak Moon
Ga-In Lee
Hoon Noh
Joon Kyo Chung
Min Chae Kang
Myung Jin Chung
Publication date
01-12-2020
Publisher
BioMed Central
Published in
BMC Ophthalmology / Issue 1/2020
Electronic ISSN: 1471-2415
DOI
https://doi.org/10.1186/s12886-020-01657-w

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