The application value of deep learning OCT on wet age-related macular degeneration assisted diagnosis

Authors:Gong Yan,  Gu Zaiwang,  Hu Yan,  Liao Yanhong,  Ye Ting,  Liu Dong,  Liu Jiang
DOI: 10.3760/cma.j.issn.2095-0160.2019.08.014
Published 2019-08-10
Cite as Chin J Exp Ophthalmol, 2019,37(8): 658-662.

Abstract

Objective

To investigate the application value of deep learning optical coherence tomography (OCT) on wet age-related macular degeneration (wAMD) assisted diagnosis.

Methods

Weakly supervised deep learning algorithms was applied on the premise that only disease or not can be provided as a marker.The OCT image automatically assisted in the diagnosis of diseased areas of wAMD, and thermograms were applied to provide a basis for doctors to detect disease areas.Based on the deep learning of weak supervision, a new network algorithm structure was proposed for detecting disease area in ophthalmic OCT images.At the same time, thermograms were adopted to improve the accuracy of the lesion map, which is the location of the lesion area.This study followed the Declaration of Helsinki.This study protocol was approved by Ethic Committee of Ningbo Eye Hospital (No.2018-YJ05). Written informed consent was obtained from each subject before entering study cohort.

Results

Resnet-based deep learning algorithm gave a diagnostic accuracy rate of 94.9% for the disease, which was much higher than that of AlexNet 85.3%, VGG 88.7%, and Google-Net 89.2%.The thermograms with different colors provided a more convenient auxiliary diagnosis basis for doctors.

Conclusions

Compared with the original classification network, which needs disease area markers as empirical knowledge, deep learning algorithm model not only provides better results in the classification of retinal diseases, but also marks potential disease areas.The lesion area provides a basis for judging the area of the lesion for the diagnosis of wAMD.

Key words:

Wet age-related macular degeneration; Disease classification; Lesion area detection; Weak supervision deep learning

Contributor Information

Gong Yan
Ningbo Eye Hospital, Ningbo 315041, China
Gu Zaiwang
Cixi Institute of BioMedical Engineering, CNITECH, CAS, Ningbo 315201, China
Hu Yan
Cixi Institute of BioMedical Engineering, CNITECH, CAS, Ningbo 315201, China
Liao Yanhong
Ningbo Eye Hospital, Ningbo 315041, China
Ye Ting
Ningbo Eye Hospital, Ningbo 315041, China
Liu Dong
Ningbo Eye Hospital, Ningbo 315041, China
Liu Jiang
Cixi Institute of BioMedical Engineering, CNITECH, CAS, Ningbo 315201, China; Department of Computer Science and Engineering, Southern University of Science and Technology, ShenZhen 518055, China
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Updated: October 9, 2019 — 6:43 am