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Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer

Nature Biomedical Engineering volume 6, pages 593–604 (2022)Cite this article

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Abstract

The clinical diagnostic evaluation of optic neuropathies relies on the analysis of the thickness of the retinal nerve fibre layer (RNFL) by optical coherence tomography (OCT). However, false positives and false negatives in the detection of RNFL abnormalities are common. Here we show that an algorithm integrating measurements of RNFL thickness and reflectance from standard wide-field OCT scans can be used to uncover the trajectories and optical texture of individual axonal fibre bundles in the retina and to discern distinctive patterns of loss of axonal fibre bundles in glaucoma, compressive optic neuropathy, optic neuritis and non-arteritic anterior ischaemic optic neuropathy. Such optical texture analysis can detect focal RNFL defects in early optic neuropathy, as well as residual axonal fibre bundles in end-stage optic neuropathy that were indiscernible by conventional OCT analysis and by red-free RNFL photography. In a diagnostic-performance study, optical texture analysis of the RNFL outperformed conventional OCT in the detection of glaucoma, as defined by visual-field testing or red-free photography. Our findings show that optical texture analysis of the RNFL for the detection of optic neuropathies is highly sensitive and specific.

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Fig. 1: Algorithm for ROTA.
Fig. 2: ROTA discerns the patterns and the extent of RNFL defects in eyes with different severity of optic nerve damage.
Fig. 3: ROTA detects focal RNFL defects missed by conventional OCT analysis.
Fig. 4: ROTA discerns the patterns and severity of axonal fibre bundle loss at the macula.
Fig. 5: ROTA reveals distinctive patterns of RNFL defects in non-glaucomatous optic neuropathies.
Fig. 6: ROTA uncovers axonal fibre bundle edema in idiopathic intracranial hypertension and RNFL defects in eyes with optic disc drusen.
Fig. 7: Comparison of diagnostic performance between ROTA and OCT pRNFLT/mGCIPLT analysis for detection of early glaucoma and glaucoma.
Fig. 8: High resolution ROTA.

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Data availability

All patient data are available from the corresponding author on reasonable request, subject to approval from the Institutional Review Board of Hong Kong Hospital Authority research ethics committees. The Cirrus OCT normative data have been described22.

Code availability

Conventional texture-based image analyses were performed using MATLAB R2018a. The texture features required for GLCM texture analysis were extracted using functions from Statistics and Machine Learning Toolbox (MATLAB R2018a), Image Processing Toolbox (MATLAB R2018a) and code (GLCMFeatures ver. 2.1.1.0 by Patrik Brynolfsso 2016, publicly available from https://www.mathworks.com/matlabcentral/fileexchange/55034-glcmfeatures-glcm). The texture feature required for wavelet texture analysis was extracted using functions from Image Processing Toolbox (MATLAB R2018a). ROTA images were generated using a custom code developed in MATLAB R2018a. The code is available from the corresponding author on reasonable request. Statistical analysis was performed with STATA (ver. 15) and R (ver. 3.4.4).

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Acknowledgements

This work was supported by the Hong Kong Research Grants Council General Research Fund 14101518, 14101117, 14100916 and 14101215.

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Authors and Affiliations

  1. Department of Ophthalmology, LKS Faculty of Medicine, The University of Hong Kong, Pok Fu Lam, Hong Kong

    Christopher Kai Shun Leung, Alexander Ka Ngai Lam, Philip Yawen Guo, Vivian Sheung Man Chiu, Ken Zhongheng Wu & Gilda Wing Ki Lai

  2. Department of Ophthalmology and Visual Sciences, The Chinese University of Hong Kong, Shatin, Hong Kong

    Christopher Kai Shun Leung, Kelvin Ho Nam Wan, Carol Yim Lui Cheung, Chen Lin, Carmen Kar Mun Chan & Noel Ching Yan Chan

  3. Queen Mary Hospital, Pok Fu Lam, Hong Kong

    Christopher Kai Shun Leung

  4. Hong Kong Eye Hospital, Kowloon City, Hong Kong

    Christopher Kai Shun Leung, Kelvin Ho Nam Wan, Mandy Wong & Carmen Kar Mun Chan

  5. Hamilton Glaucoma Center, Viterbi Family Department of Ophthalmology and Shiley Eye Institute, University of California, San Diego, CA, USA

    Robert Neal Weinreb

  6. NIHR Biomedical Research Centre at Moorfields Eye Hospital NHS Foundation Trust and UCL Institute of Ophthalmology, London, UK

    David F. Garway-Heath

  7. Singapore Eye Research Institute, Singapore, Singapore

    Marco Yu

  8. Department of Ophthalmology, Tuen Mun Hospital, Tuen Mun, Hong Kong

    Kelvin Ho Nam Wan

  9. Department of Ophthalmology, Prince of Wales Hospital, Shatin, Hong Kong

    Noel Ching Yan Chan & Ka Wai Kam

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Contributions

C.K.S.L. conceptualized and designed the study; C.K.S.L. and A.K.N.L. developed the algorithm of ROTA; K.H.N.W., M.W., C.Y.L.C., C.K.M.C., N.C.Y.C., K.W.K. and C.K.S.L. recruited study participants; C.K.S.L., M.Y., A.K.N.L. and P.Y.G. performed data analysis; C.K.S.L. wrote the manuscript; all authors discussed the results, reviewed and edited the manuscript.

Corresponding author

Correspondence to Christopher Kai Shun Leung.

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Competing interests

C.K.S.L. and A.K.N.L. hold a patent (US patent 62/571,559) for ROTA and are founders of AIROTA Diagnostics Limited. A licensing agreement is under discussion between the Chinese University of Hong Kong and Heidelberg Engineering (Heidelberg, Germany) and Carl Zeiss Meditec (Dublin, CA, United States). C.K.S.L. has received research support in the form of instruments, research grants and speaker honoraria from Carl Zeiss Meditec, Heidelberg Engineering and Topcon (Tokyo, Japan). R.N.W. has received instruments from Carl Zeiss Meditec, Heidelberg Engineering and Optovue (Fremont, CA, United States).

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Leung, C.K.S., Lam, A.K.N., Weinreb, R.N. et al. Diagnostic assessment of glaucoma and non-glaucomatous optic neuropathies via optical texture analysis of the retinal nerve fibre layer. Nat. Biomed. Eng 6, 593–604 (2022). https://doi.org/10.1038/s41551-021-00813-x

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