■我们介绍了一种基于深度学习的生物标志物提议系统,旨在加速年龄相关性黄斑变性(AMD)中的生物标志物发现。
■视网膜OCT图像的大型数据集的回顾性分析。
■共有3456名年龄在51至102岁之间的成年人在PINNACLE项目下收集OCT图像。
■我们的系统提出了OCT中新型AMD成像生物标志物的候选物。它的工作原理是首先使用自监督对比学习来训练神经网络,没有任何临床注释,46496张视网膜OCT图像中与已知和未知AMD生物标志物相关的特征。为了解释学习的生物标志物,我们将图像分成30个子集,称为集群,包含类似特征的。我们与2个独立的视网膜专家团队进行了2次平行的1.5小时半结构化访谈,以临床语言为每个集群分配描述。实现共识的聚类的描述可以潜在地告知新的生物标志物候选。
■我们检查了每个集群是否显示出视网膜专家可以理解的清晰特征,如果它们与AMD有关,以及与最近提出的或潜在的新生物标志物相比,有多少描述的已建立的生物标志物用于分级系统。我们还将其对晚期湿性和干性AMD的预后价值与已建立的临床分级系统和人口统计学基线模型进行了比较。
■总的来说,两个团队独立地确定了30个集群中的27个明显不同的特征,其中23人与AMD有关。七个被认为是已建立的分级系统中使用的已知生物标志物,和16描绘了尚未在分级系统中使用的生物标志物组合或亚型,是最近才提出的,或者是未知的。从完全视网膜萎缩中分离出不完全的簇,来自视网膜下液的视网膜内,从薄薄的脉络膜变厚,and,在模拟中,在预后价值方面优于临床使用的分级系统。
■使用自监督深度学习,我们能够自动提出超出临床建立的分级系统中使用的范围的AMD生物标志物.没有任何临床注释,对比学习发现细粒度生物标志物之间的细微差别。最终,我们设想,为临床医生配备面向发现的深度学习工具可以加速发现新的预后生物标志物.
■专有或商业披露可在本文末尾的脚注和披露中找到。
UNASSIGNED: We introduce a deep learning-based biomarker proposal system for the purpose of accelerating biomarker discovery in age-related macular degeneration (
AMD).
UNASSIGNED: Retrospective analysis of a large data set of retinal OCT images.
UNASSIGNED: A total of 3456 adults aged between 51 and 102 years whose OCT images were collected under the PINNACLE project.
UNASSIGNED: Our system proposes candidates for novel
AMD imaging biomarkers in OCT. 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 OCT images. To interpret the learned biomarkers, we partition the images into 30 subsets, termed clusters, that contain similar features. We conduct 2 parallel 1.5-hour semistructured interviews with 2 independent teams of retinal specialists to assign descriptions in clinical language to each cluster. Descriptions of clusters achieving consensus can potentially inform new biomarker candidates.
UNASSIGNED: We checked if each cluster showed clear features comprehensible to retinal specialists, if they related to AMD, and how many described established biomarkers used in grading systems as opposed to recently proposed or potentially new biomarkers. We also compared their prognostic value for late-stage wet and dry
AMD against an established clinical grading system and a demographic baseline model.
UNASSIGNED: Overall, both teams independently identified clearly distinct characteristics in 27 of 30 clusters, of which 23 were related to AMD. Seven were recognized as known biomarkers 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.
UNASSIGNED: Using self-supervised deep learning, we were able to automatically propose
AMD biomarkers going beyond the set used in clinically established grading systems. Without any clinical annotations, contrastive learning discovered subtle differences between fine-grained biomarkers. Ultimately, we envision that equipping clinicians with discovery-oriented deep learning tools can accelerate the discovery of novel prognostic biomarkers.
UNASSIGNED: Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.