Clustering disease trajectories in contrastive feature space for biomarker proposal in age-related macular degeneration

Holland, Robbie, Leingang, Oliver, Holmes, Christopher, Anders, Philipp, Kaye, Rebecca, Riedl, Sophie, Paetzold, Johannes C., Ezhov, Ivan, Bogunović, Hrvoje, Schmidt-Erfurth, Ursula, Scholl, Hendrik P.N., Sivaprasad, Sobha, Lotery, Andrew J., Rueckert, Daniel and Menten, Martin (2023) Clustering disease trajectories in contrastive feature space for biomarker proposal in age-related macular degeneration. Greenspan, Hayit, Greenspan, Hayit, Madabhushi, Anant, Mousavi, Parvin, Salcudean, Septimiu, Duncan, James, Syeda-Mahmood, Tanveer and Taylor, Russell (eds.) In Medical Image Computing and Computer Assisted Intervention – MICCAI 2023 - 26th International Conference, Proceedings: 26th International Conference, Vancouver, BC, Canada, October 8–12, 2023, Proceedings, Part VII. vol. 14226, Springer Cham. pp. 724-734 . (doi:10.1007/978-3-031-43990-2_68).

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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Contributors

Author: Rebecca Kaye

Author: Andrew J. Lotery

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