Glaucoma progression prediction using retinal thickness via latent space linear regression

Yuhui Zheng, Jing Wang, Linchuan Xu, Hiroshi Murata, Kenji Yamanishi, Taichi Kiwaki, Ryo Asaoka

Research output: Chapter in book / Conference proceedingConference article published in proceeding or bookAcademic researchpeer-review

1 Citation (Scopus)

Abstract

Prediction of glaucomatous visual field loss has significant clinical benefits because it can help with early detection of glaucoma as well as decision-making for treatments. Glaucomatous visual loss is conventionally captured through visual field sensitivity (VF) measurement, which is costly and time-consuming. Thus, existing approaches mainly predict future VF utilizing limited VF data collected in the past. Recently, optical coherence tomography (OCT) has been adopted to measure retinal layers thickness (RT) for considerably more low-cost treatment assistance. There then arises an important question in the context of ophthalmology: are RT measurements beneficial for VF prediction? In this paper, we propose a novel method to demonstrate the benefits provided by RT measurements. The challenge is management of the two heterogeneities of VF data and RT data as RT data are collected according to different clinical schedules and lie in a different space to VF data. To tackle these heterogeneities, we propose latent progression patterns (LPPs), a novel type of representations for glaucoma progression. Along with LPPs, we propose a method to transform VF series to an LPP based on matrix factorization and a method to transform RT series to an LPP based on deep neural networks. Partial VF and RT information is integrated in LPPs to provide accurate prediction. The proposed framework is named deeply-regularized latent-space linear regression (DLLR). We empirically demonstrate that our proposed method outperforms the state-of-the-art technique by 12% for the best case in terms of the mean of the root mean square error on a real dataset.

Original languageEnglish
Title of host publicationKDD 2019 - Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining
PublisherAssociation for Computing Machinery
Pages2278-2286
Number of pages9
ISBN (Electronic)9781450362016
DOIs
Publication statusPublished - 25 Jul 2019
Externally publishedYes
Event25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019 - Anchorage, United States
Duration: 4 Aug 20198 Aug 2019

Publication series

NameProceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining

Conference

Conference25th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, KDD 2019
Country/TerritoryUnited States
CityAnchorage
Period4/08/198/08/19

Keywords

  • Convolutional Neural Networks
  • Coupled Matrix Factorization
  • Glaucoma Progression Prediction
  • Multi-view Learning
  • Regression
  • Regularization

ASJC Scopus subject areas

  • Software
  • Information Systems

Cite this