Unbiased identification of novel subclinical imaging biomarkers using unsupervised deep learning.

ABSTRACT: Artificial intelligence has recently made a disruptive impact in medical imaging by successfully automatizing expert-level diagnostic tasks. However, replicating human-made decisions may inherently be biased by the fallible and dogmatic nature of human experts, in addition to requiring prohibitive amounts of training data. In this paper, we introduce an unsupervised deep learning architecture particularly designed for OCT representations for unbiased, purely data-driven biomarker discovery. We developed artificial intelligence technology that provides biomarker candidates without any restricting input or domain knowledge beyond raw images. Analyzing 54,900 retinal optical coherence tomography (OCT) volume scans of 1094 patients with age-related macular degeneration, we generated a vocabula

SUBMITTER: Waldstein SM 

PROVIDER: S-EPMC7395081 | biostudies-literature | 2020 Jul

REPOSITORIES: biostudies-literature