Deep Learning-Based Noise Reduction Improves Optical Coherence Tomography Angiography Imaging of Radial Peripapillary Capillaries in Advanced Glaucoma

Abstract

Purpose

We applied deep learning-based noise reduction (NR) to optical coherence tomography-angiography (OCTA) images of the radial peripapillary capillaries (RPCs) in eyes with glaucoma and investigated the usefulness of this method as an objective analysis of glaucoma.

Methods

This cross-sectional study included 118 eyes of 94 open-angle glaucoma patients (male/female = 38/56, age: 56.1 ± 10.3 years). We used OCTA (OCT-HS100, Canon) and built-in software (RX software, v. 4.5) to perform NR and calculate RPC vessel area density (VAD) and skeleton vessel length density (VLD). We also examined NR’s effect on reproducibility. Finally, we assessed the vascular structure (PRCs)/function relationship at different glaucoma stages with Spearman’s correlation.

Results

Regardless of NR, RPC parameters had excellent coefficients of variation (1.7–4.1%) in glaucoma patients and controls, and mean deviation (MD) was significantly correlated with VAD (NR: r = 0.835, p < 0.001; non-NR: r = 0.871, p < 0.001) and VLD (NR: r = 0.829, p < 0.001; non-NR: r = 0.837, p < 0.001). For mild, moderate, and advanced glaucoma, the correlation coefficients between MD and VLD were 0.366 (p = 0.028) 0.081 (p = 0.689), and 0.427 (p = 0.017) with NR and 0.405 (p = 0.014), 0.184 (p = 0.360), and 0.339 (p = 0.062) without NR, respectively.

Conclusion

Denoised RPC images might have the potential for a closer structural/functional relationship, in which the floor effect of retinal nerve fiber layer thickness affects measurements. Deep learning-based NR promises to improve glaucoma assessment.

Keywords:

• Optical coherence tomography angiography
• radial peripapillary capillary density
• assessment of advanced glaucoma
• deep learning
• diagnosis

Acknowledgements

The authors thank Mr. Tim Hilts for editing this manuscript and Erika Kawamoto for technical support.

Ethical approval

Our ethics committee allowed us to waive written informed consent from the patients, because we used retrospective data from patients who received insured medical treatment and opted out of consent requests. No patient was individually identified in this study.

Author contributions

KO and HT contributed to data collection. KO and TN contributed to writing the manuscript. JH provided technical assistance.

Disclosure statement

JH is employed by Canon Corporation, a commercial company, but declares no conflicts of interest in association with the content of this article. No potential conflict of interest was reported by the author(s).

Data availability statement

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Additional information

Funding

This manuscript was supported in part by a JST grant from JSPS Kakenhi Grants-in-Aid for Scientific Research (B) [T.N. 26293372], by the JST Center for Revitalization Promotion and Kakenhi Grants-in-Aid for young scientists (B) [K.O. 17K16957], by the Public Trust Suda Memorial Fund, and by the Kitazawa Yoshiaki Glaucoma Research Award.