Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)

• URL: http://arxiv.org/abs/2405.09549v1
• Date: Tue, 12 Mar 2024 13:48:17 GMT
• Title: Deep-learning-based clustering of OCT images for biomarker discovery in age-related macular degeneration (Pinnacle study report 4)
• Authors: Robbie Holland, Rebecca Kaye, Ahmed M. Hagag, Oliver Leingang, Thomas R. P. Taylor, Hrvoje Bogunović, Ursula Schmidt-Erfurth, Hendrik P. N. Scholl, Daniel Rueckert, Andrew J. Lotery, Sobha Sivaprasad, Martin J. Menten,
• Abstract summary: We introduce a deep-learning-based biomarker proposal system for age-related macular degeneration (AMD) It works by first training a neural network using self-supervised contrastive learning to discover features relating to both known and unknown AMD biomarkers.
• Score: 7.932410831191909
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Diseases are currently managed by grading systems, where patients are stratified by grading systems into stages that indicate patient risk and guide clinical management. However, these broad categories typically lack prognostic value, and proposals for new biomarkers are currently limited to anecdotal observations. In this work, we introduce a deep-learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (AMD). It works by first training a neural network using self-supervised contrastive learning to discover, without any clinical annotations, features relating to both known and unknown AMD biomarkers present in 46,496 retinal optical coherence tomography (OCT) images. To interpret the discovered biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We then conduct two parallel 1.5-hour semi-structured interviews with two independent teams of retinal specialists that describe each cluster in clinical language. Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognised as known biomarkers already used in established grading systems and 16 depicted biomarker combinations or subtypes that are either not yet used in grading systems, were only recently proposed, or were unknown. Clusters separated incomplete from complete retinal atrophy, intraretinal from subretinal fluid and thick from thin choroids, and in simulation outperformed clinically-used grading systems in prognostic value. Overall, contrastive learning enabled the automatic proposal of AMD biomarkers that go beyond the set used by clinically established grading systems. Ultimately, we envision that equipping clinicians with discovery-oriented deep-learning tools can accelerate discovery of novel prognostic biomarkers.

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