Patterns of retinal nerve fiber layer loss in patients with glaucoma identified by deep archetypal analysis

Sidharth Mahotra, Mengyu Wang, Tobias Elze, Michael V. Boland, Louis Pasquale, Juleke Majoor, Koen A. Vermeer, Chris Johnson, Kouros Nouri-Mahdavi, Hans Lemij, Micahel Goldbaum, Siamak Yousefi

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

Glaucoma is a complex eye disorder characterized by an optic neuropathy usually leading to typical patterns of structural and functional loss. Current classification of glaucoma damage is predominantly subjective and qualitative. Determining precise glaucoma-induced patterns of structural and functional loss is clinically significant because different patterns of loss could differentially impact patient quality of life. Here, we develop and apply deep archetypal analysis (DAA) to over 2,500 samples of optical coherence tomography (OCT) images around the optic disc of about 278 eyes with glaucoma to discover patterns of structural loss. We show that deep DAA is an appropriate approach for discovering patterns on the convex hull that encloses data points in a high-dimensional space, and that this approach is resistant to outliers. We also present a novel visualization with potential utility in clinical applications for assessing structural damage in patients with glaucoma. Compared to classical archetypal matrix decomposition, DAA discovers outlier-resistant patterns. Unlike deep learning models, DAA generates interpretable outcomes with clinical relevance. Finally, 16 discovered patterns of RNFL loss are visualized and clinically validated by glaucoma experts. Such patterns may serve as basic elements to quantify high-dimensional RNFL data in different applications.

Original languageEnglish (US)
Title of host publicationProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
EditorsXintao Wu, Chris Jermaine, Li Xiong, Xiaohua Tony Hu, Olivera Kotevska, Siyuan Lu, Weijia Xu, Srinivas Aluru, Chengxiang Zhai, Eyhab Al-Masri, Zhiyuan Chen, Jeff Saltz
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages3775-3782
Number of pages8
ISBN (Electronic)9781728162515
DOIs
StatePublished - Dec 10 2020
Event8th IEEE International Conference on Big Data, Big Data 2020 - Virtual, Atlanta, United States
Duration: Dec 10 2020Dec 13 2020

Publication series

NameProceedings - 2020 IEEE International Conference on Big Data, Big Data 2020

Conference

Conference8th IEEE International Conference on Big Data, Big Data 2020
CountryUnited States
CityVirtual, Atlanta
Period12/10/2012/13/20

Keywords

  • artificial intelligence
  • Big data
  • deep archetypal analysis
  • glaucoma
  • retinal nerve fiber layer thickness

ASJC Scopus subject areas

  • Computer Networks and Communications
  • Information Systems
  • Information Systems and Management
  • Safety, Risk, Reliability and Quality

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