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Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration

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Abstract

Age-related macular degeneration (AMD) is the leading cause of blindness in the elderly. Current grading systems based on imaging biomarkers only coarsely group disease stages into broad categories that lack prognostic value for future disease progression. It is widely believed that this is due to their focus on a single point in time, disregarding the dynamic nature of the disease. In this work, we present the first method to automatically propose biomarkers that capture temporal dynamics of disease progression. Our method represents patient time series as trajectories in a latent feature space built with contrastive learning. Then, individual trajectories are partitioned into atomic sub-sequences that encode transitions between disease states. These are clustered using a newly introduced distance metric. In quantitative experiments we found our method yields temporal biomarkers that are predictive of conversion to late AMD. Furthermore, these clusters were highly interpretable to ophthalmologists who confirmed that many of the clusters represent dynamics that have previously been linked to the progression of AMD, even though they are currently not included in any clinical grading system.

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Acknowledgements

The PINNACLE study is funded by a Wellcome Trust Collaborative Award (ref. 210572/Z/18/Z). This work is also funded by the Munich Center for Machine Learning.

Author information

Authors and Affiliations

  1. BioMedIA, Imperial College London, London, UK

    Robbie Holland, Johannes C. Paetzold, Daniel Rueckert & Martin J. Menten

  2. Laboratory for Ophthalmic Image Analysis, Medical University of Vienna, Vienna, Austria

    Oliver Leingang, Sophie Riedl, Hrvoje Bogunović & Ursula Schmidt-Erfurth

  3. Moorfields Eye Hospital NHS Foundation Trust, London, UK

    Christopher Holmes & Sobha Sivaprasad

  4. Institute of Molecular and Clinical Ophthalmology Basel, Basel, Switzerland

    Philipp Anders & Hendrik P. N. Scholl

  5. Department of Ophthalmology, Universitat Basel, Basel, Switzerland

    Hendrik P. N. Scholl

  6. Clinical and Experimental Sciences, University of Southampton, Southampton, UK

    Rebecca Kaye & Andrew J. Lotery

  7. Technical University of Munich, Munich, Germany

    Ivan Ezhov, Daniel Rueckert & Martin J. Menten

Authors
  1. Robbie Holland

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  2. Oliver Leingang

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  3. Christopher Holmes

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  4. Philipp Anders

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  5. Rebecca Kaye

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  6. Sophie Riedl

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  7. Johannes C. Paetzold

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  8. Ivan Ezhov

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  9. Hrvoje Bogunović

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  10. Ursula Schmidt-Erfurth

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  11. Hendrik P. N. Scholl

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  12. Sobha Sivaprasad

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  13. Andrew J. Lotery

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  14. Daniel Rueckert

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  15. Martin J. Menten

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Corresponding author

Correspondence toRobbie Holland.

Editor information

Editors and Affiliations

  1. Icahn School of Medicine, Mount Sinai, NYC, NY, USA, Tel Aviv University, Tel Aviv, Israel

    Hayit Greenspan

  2. Emory University, Atlanta, GA, USA

    Anant Madabhushi

  3. Queen’s University, Kingston, ON, Canada

    Parvin Mousavi

  4. The University of British Columbia, Vancouver, BC, Canada

    Septimiu Salcudean

  5. Yale University, New Haven, CT, USA

    James Duncan

  6. IBM Research, San Jose, CA, USA

    Tanveer Syeda-Mahmood

  7. Johns Hopkins University, Baltimore, MD, USA

    Russell Taylor

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© 2023 The Author(s), under exclusive license to Springer Nature Switzerland AG

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Holland, R.et al. (2023). Clustering Disease Trajectories in Contrastive Feature Space for Biomarker Proposal in Age-Related Macular Degeneration. In: Greenspan, H.,et al. Medical Image Computing and Computer Assisted Intervention – MICCAI 2023. MICCAI 2023. Lecture Notes in Computer Science, vol 14226. Springer, Cham. https://doi.org/10.1007/978-3-031-43990-2_68

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