Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from the Maastricht study

Friso G. Heslinga, Josien P.W. Pluim, A. J.H.M. Houben, Miranda T. Schram, Ronald M.A. Henry, Coen D.A. Stehouwer, Marleen J. Van Greevenbroek, Tos T.J.M. Berendschot, Mitko Veta

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Abstract

Type 2 Diabetes (T2D) is a chronic metabolic disorder that can lead to blindness and cardiovascular disease. Information about early stage T2D might be present in retinal fundus images, but to what extent these images can be used for a screening setting is still unknown. In this study, deep neural networks were employed to differentiate between fundus images from individuals with and without T2D. We investigated three methods to achieve high classification performance, measured by the area under the receiver operating curve (ROC-AUC). A multi-target learning approach to simultaneously output retinal biomarkers as well as T2D works best (AUC = 0.746 [±0.001]). Furthermore, the classification performance can be improved when images with high prediction uncertainty are referred to a specialist. We also show that the combination of images of the left and right eye per individual can further improve the classification performance (AUC = 0.758 [±0.003]), using a simple averaging approach. The results are promising, suggesting the feasibility of screening for T2D from retinal fundus images.

Original languageEnglish
Title of host publicationMedical Imaging 2020
Subtitle of host publicationComputer-Aided Diagnosis
EditorsHorst K. Hahn, Maciej A. Mazurowski
PublisherSPIE
Chapter6
ISBN (Electronic)9781510633957
DOIs
Publication statusPublished - 1 Jan 2020
EventSPIE Medical Imaging 2020 - Houston, United States
Duration: 15 Feb 202020 Feb 2020

Publication series

NameProceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceSPIE Medical Imaging 2020
Country/TerritoryUnited States
CityHouston
Period15/02/2020/02/20

Funding

This research is financially supported by the TTW Perspectief program and Philips Research. The authors have no conflicts of interests to report. This work has not been submitted for publication anywhere else. The clinical data used in the research originates from the Maastricht Study. The Maastricht Study was supported by the European Regional Development Fund via OP-Zuid, the Province of Limburg, the Dutch Ministry of Economic Affairs (grant 31O.041), Stichting De Weijerhorst (Maastricht, The Netherlands), the Pearl String Initiative Diabetes (Amsterdam, The Netherlands), CARIM School for Cardiovascular Diseases (Maastricht, The Netherlands), Stichting Annadal (Maastricht, The Netherlands), Health Foundation Limburg (Maastricht, The Netherlands) and by unrestricted grants from Janssen-Cilag B.V. (Tilburg, The Netherlands), Novo Nordisk Farma B.V. (Alphen aan den Rijn, The Netherlands), and Sanofi-Aventis Netherlands B.V. (Gouda, The Netherlands).

Keywords

  • classication uncertainty
  • deep learning
  • retinal image analysis
  • the Maastricht study
  • type 2 diabetes

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