Clustering disease trajectories in contrastive feature space for biomarker discovery in age-related macular degeneration
This addresses the challenge of coarse disease staging in AMD for clinicians and patients, offering a novel approach to capture dynamic progression, though it is incremental in applying contrastive learning and clustering to this domain.
The paper tackled the problem of predicting disease progression in age-related macular degeneration by developing a method to automatically discover temporal biomarkers from patient time series, resulting in clusters that were predictive of conversion to late AMD and interpretable to ophthalmologists.
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 and are unable to predict 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 discover 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.