A Dual-Modal Fusion Network Using Optical Coherence Tomography and Fundus Images in Detection of Glaucomatous Optic Neuropathy

Abstract

Purpose

We designed a dual-modal fusion network to detect glaucomatous optic neuropathy, which utilized both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images.

Methods

A total of 327 healthy subjects (410 eyes) and 87 glaucomatous optic neuropathy patients (113 eyes) were included. The retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images were used as predictors in the dual-modal fusion network to diagnose glaucoma. The area under the receiver operation characteristic curve, accuracy, sensitivity, and specificity were measured to compare our method and other approaches.

Results

The accuracy of our dual-modal fusion network using both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images was 0.935 and we achieved a significant larger area under the receiver operation characteristic curve of our method with 0.968 (95% confidence interval, 0.937–0.999). For only using retinal nerve fiber layer thickness, we compared the area under the receiver operation characteristic curves between our network and other three approaches: 0.916 (95% confidence interval, 0.855, 0.977) with our optical coherence tomography Net; 0.841 (95% confidence interval, 0.749, 0.933) with Clock sectors division; 0.862 (95% confidence interval, 0.757, 0.968) with inferior, superior, nasal temporal sectors division and 0.886 (95% confidence interval, 0.815, 0.957) with optic disc sectors division. For only using fundus images, we compared the area under the receiver operation characteristic curves between our network and other two approaches: 0.867 (95% confidence interval: 0.781–0.952) with our Image Net; 0.774 (95% confidence interval: 0.670, 0.878) with ResNet50; 0.747 (95% confidence interval: 0.628, 0.866) with VGG16.

Conclusion

Our dual-modal fusion network utilizing both retinal nerve fiber layer thickness from optical coherence tomography reports and fundus images can diagnose glaucoma with a much better performance than the current approaches based on optical coherence tomography only or fundus images only.

Keywords:

• Deep learning
• fusion network
• glaucoma
• fundus image
• optical coherence tomography

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data that support the findings of this study are available from the corresponding author upon reasonable request.

Additional information

Funding

This research received no specific grant from any funding agency in the public, commercial, or not-for-profit sectors.