July 2018
Volume 59, Issue 9
Open Access
ARVO Annual Meeting Abstract  |   July 2018
Increase the classification and expression ability and visualize the decision through a novel deep neural network model for the diagnosis of glaucoma
Author Affiliations & Notes
  • Jicong Zhang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China
    Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
  • Hua Wang
    School of Biological Science and Medical Engineering, Beihang University, Beijing, China
    Beijing Advanced Innovation Centre for Biomedical Engineering, Beihang University, Beijing, China
  • Haogang Zhu
    School of Computer Science and Engineering, Beihang University, Beijing, China
    Beijing Advanced Innovation Centre for Big Data Based Precision Medicine, Beihang University, Beijing, China
  • Footnotes
    Commercial Relationships   Jicong Zhang, None; Hua Wang, None; Haogang Zhu, None
  • Footnotes
    Support  National Key R&D program of China (2016YFF0201002)
Investigative Ophthalmology & Visual Science July 2018, Vol.59, 4079. doi:
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    • Get Citation

      Jicong Zhang, Hua Wang, Haogang Zhu; Increase the classification and expression ability and visualize the decision through a novel deep neural network model for the diagnosis of glaucoma. Invest. Ophthalmol. Vis. Sci. 2018;59(9):4079.

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      © ARVO (1962-2015); The Authors (2016-present)

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Abstract

Purpose : The irreversible blinding of glaucoma makes it a critical need for the patient for an early and accurate diagnosis of glaucoma. While the state-of-art deep neural network is developed rapidly, we propose a new network model and make it applicable to the computer-aided diagnosis based on the data of Heidelberg Retina Tomograph (HRT) of glaucoma with weak labels, which includes HRT images of two modalities: the reflection intensity of light and the topographic value.

Methods : We propose a novel deep network model to make full use of the HRT data with weak labels, which represent the characteristics of two types of images: 1. the reflection intensity of light, 2. the topographic value. The design of our network model can solve the ambiguity of the two types of images when they are input together and maintain the subtle differences when the two types of images are input separately. In addition, the visualization technique is used for the proposed network of normal subjects versus glaucoma ones, which could show the supported evidence for the decision of the network.

Results : After training the proposed deep neural network model for the diagnosis of glaucoma using the HRT images with weak labels, the independent test experiment made 94.0% of the area under the ROC curve (AUC), which means at least a 5% increase compared with the previous results. More importantly, the sensitivity is 91.2% when the specificity is 0.85 and is 78.3% when the specificity is 0.95, which implies that our method may potentially be used for the clinical screening as an indicator for the early diagnosis of glaucoma.

Conclusions : Our work can change the diagnositic mode of glaucoma using HRT data. Independent test results show that the proposed network model can contribute to an accurate diagnosis of glaucoma (especially in the early stage) with HRT. An early and accurate diagnosis of glaucoma may lead to early and precise treatment for the patients with glaucoma, and may result in an increase in their quality of life.

This is an abstract that was submitted for the 2018 ARVO Annual Meeting, held in Honolulu, Hawaii, April 29 - May 3, 2018.

 

ROC curve of the proposed network model for detection of glaucoma compared with different model designs (left) and with several classic network models (right)

ROC curve of the proposed network model for detection of glaucoma compared with different model designs (left) and with several classic network models (right)

 

Visualization (maps and evidences) of the effects of typical cases of normal and glaucoma HRT images (two modalities: reflection and topology) using the proposed deep neural network

Visualization (maps and evidences) of the effects of typical cases of normal and glaucoma HRT images (two modalities: reflection and topology) using the proposed deep neural network

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