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

arXiv: https://arxiv.org/abs/2301.04525 · HTML: https://mlsanity.com/paper/2301.04525/

## Key facts

- Cited by 3 papers in the map (data through 14 Oct 2026).
- Builds on 7 of its 31 references that are in the map.
- First posted on arXiv 11 Jan 2023.

## Builds on (most cited first, 7 of 7)

- [A Simple Framework for Contrastive Learning of Visual Representations](https://mlsanity.com/paper/2002.05709/) (2020)
- [Bootstrap your own latent: A new approach to self-supervised Learning](https://mlsanity.com/paper/2006.07733/) (2020)
- [Soft-DTW: a Differentiable Loss Function for Time-Series](https://mlsanity.com/paper/1703.01541/) (2017)
- [Metadata-enhanced contrastive learning from retinal optical coherence tomography images](https://mlsanity.com/paper/2208.02529/) (2022)
- [A survey on trajectory clustering analysis](https://mlsanity.com/paper/1802.06971/) (2018)
- [Unsupervised Identification of Disease Marker Candidates in Retinal OCT Imaging Data](https://mlsanity.com/paper/1810.13404/) (2018)
- [Self-supervised learning for analysis of temporal and morphological drug effects in cancer cell imaging data](https://mlsanity.com/paper/2203.04289/) (2022)

## Built on by (most cited first, 3 of 3)

- [A Comprehensive Review of Artificial Intelligence Applications in Major Retinal Conditions](https://mlsanity.com/paper/2311.13710/) (2023)
- [Clustering of Disease Trajectories with Explainable Machine Learning: A Case Study on Postoperative Delirium Phenotypes](https://mlsanity.com/paper/2405.03327/) (2024)
- [Semi-Supervised Generative Models for Disease Trajectories: A Case Study on Systemic Sclerosis](https://mlsanity.com/paper/2407.11427/) (2024)

## Abstract (arXiv)

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.