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Classifying glaucoma exclusively with OCT: comparison of three clustering algorithms derived from machine learning

Eye volume 38, pages 841–846 (2024)Cite this article

Subjects

Abstract

Background/Aims

To objectively classify eyes as either healthy or glaucoma based exclusively on data provided by peripapillary retinal nerve fiber layer (pRNFL) and ganglion cell-inner plexiform (GCIPL) measurements derived from spectral-domain optical coherence tomography (SD-OCT) using machine learning algorithms.

Methods

Three clustering methods (k-means, hierarchical cluster analysis -HCA- and model-based clustering-MBC-) were used separately to classify a training sample of 109 eyes as either healthy or glaucomatous using solely 13 SD-OCT parameters: pRNFL average and sector thicknesses and GCIPL average and minimum values together with the six macular wedge-shaped regions. Then, the best-performing algorithm was applied to an independent test sample of 102 eyes to derive close estimates of its actual performance (external validation).

Results

In the training sample, accuracy was 91.7% for MBC, 81.7% for k-means and 78.9% for HCA (p value = 0.02). The best MBC model was that in which subgroups were allowed to have variable volume and shape and equal orientation. The MBC algorithm in the independent test sample correctly classified 98 out of 102 cases for an overall accuracy of 96.1% (95% CI, 92.3–99.8%), with a sensitivity of 94.3 and 100% specificity. The accuracy for pRNFL was 92.2% (95% CI, 86.9–97.4%) and for GCIPL 98.0% (95% CI, 95.3–100%).

Conclusions

Clustering algorithms in general (and MBC in particular) seem promising methods to help discriminate between healthy and glaucomatous eyes using exclusively SD-OCT-derived parameters. Understanding the relative merits of one method over others may also provide insights into the nature of the disease.

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Fig. 1: Venn diagrams for model-based clustering in patients diagnosed with glaucoma as compared with pRNFL and GCIPL.

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Data availability

All data generated or analyzed during this study are included in this published article (and its Supplementary Information files).

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Author information

Author notes

  1. These authors contributed equally: Marc Biarnés, Néstor Ventura-Abreu

Authors and Affiliations

  1. Oftalmologia Mèdica i Quirúrgica (OMIQ) Research, Sant Cugat del Vallès, Spain

    Marc Biarnés

  2. Institut de la Màcula (Hospital Quirón-Teknon), Barcelona, Spain

    Marc Biarnés

  3. Institute of Ophthalmology. Hospital Clínic Barcelona, Barcelona, Spain

    Néstor Ventura-Abreu, Francesc Franquesa-Garcia, Sofia Batlle-Ferrando, María Teresa Carrión-Donderis, Rafael Castro-Domínguez, Elena Millá, María Jesús Muniesa & Marta Pazos

  4. Institut d’Investigacions Biomèdiques August Pi i Sunyer (IDIBAPS), Barcelona, Spain

    Néstor Ventura-Abreu, Elena Millá, María Jesús Muniesa & Marta Pazos

  5. Hospital Sagrat Cor, Barcelona, Spain

    Néstor Ventura-Abreu

  6. Instituto Oftalmológico Fernández-Vega. Fundación de Investigación Oftalmológica, University of Oviedo, Oviedo, Spain

    Ignacio Rodríguez-Una

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  1. Marc Biarnés
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Contributions

MB contributed to design of the study, data analysis, interpretation, and writing. NVA was responsible for data acquisition and collection, data analysis, interpretation, and writing. RCD, FF, SBF, MTCD, and IRU collected data. MJM and EM data interpretation, review. MP contributed to conception and design, data acquisition, interpretation, writing and preparation of the manuscript.

Corresponding author

Correspondence to Marta Pazos.

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Competing interests

MB, FF, SBF, MTCD, RCD, EM, MJM: none. NVA and IRU: Lecturer for Carl Zeiss Meditec. MP: Lecturer and Consultant Carl Zeiss Meditec, Editorial board member for Eye.

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Biarnés, M., Ventura-Abreu, N., Rodríguez-Una, I. et al. Classifying glaucoma exclusively with OCT: comparison of three clustering algorithms derived from machine learning. Eye 38, 841–846 (2024). https://doi.org/10.1038/s41433-023-02785-5

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