TY - GEN
T1 - Patterns of retinal nerve fiber layer loss in patients with glaucoma identified by deep archetypal analysis
AU - Mahotra, Sidharth
AU - Wang, Mengyu
AU - Elze, Tobias
AU - Boland, Michael V.
AU - Pasquale, Louis
AU - Majoor, Juleke
AU - Vermeer, Koen A.
AU - Johnson, Chris
AU - Nouri-Mahdavi, Kouros
AU - Lemij, Hans
AU - Goldbaum, Micahel
AU - Yousefi, Siamak
N1 - Funding Information:
ACKNOWLEDGMENT The authors were funded by National Institute of Health (NIH), National Eye Institute (NEI) grant R21EY031725 (SY), R21EY030142 (SY, TE, MB, LRP), R01 EY015473 (LRP) and in part by an unrestricted grant from Research to Prevent Blindness (RPB), New York, NY and BrightFocus Foundation (TE), NEI R01EY030575 (TE), NIH K99 EY028631 (MW), and P30EY003790 (TE). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.
Publisher Copyright:
© 2020 IEEE.
PY - 2020/12/10
Y1 - 2020/12/10
N2 - 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.
AB - 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.
KW - Big data
KW - artificial intelligence
KW - deep archetypal analysis
KW - glaucoma
KW - retinal nerve fiber layer thickness
UR - http://www.scopus.com/inward/record.url?scp=85103852419&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85103852419&partnerID=8YFLogxK
U2 - 10.1109/BigData50022.2020.9378395
DO - 10.1109/BigData50022.2020.9378395
M3 - Conference contribution
AN - SCOPUS:85103852419
T3 - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
SP - 3775
EP - 3782
BT - Proceedings - 2020 IEEE International Conference on Big Data, Big Data 2020
A2 - Wu, Xintao
A2 - Jermaine, Chris
A2 - Xiong, Li
A2 - Hu, Xiaohua Tony
A2 - Kotevska, Olivera
A2 - Lu, Siyuan
A2 - Xu, Weijia
A2 - Aluru, Srinivas
A2 - Zhai, Chengxiang
A2 - Al-Masri, Eyhab
A2 - Chen, Zhiyuan
A2 - Saltz, Jeff
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 8th IEEE International Conference on Big Data, Big Data 2020
Y2 - 10 December 2020 through 13 December 2020
ER -