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

Research output: Chapter in Book/Report/Conference proceedingConference contributionAcademicpeer-review

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
EventMedical Imaging 2020: Computer-Aided Diagnosis - Houston, United States
Duration: 16 Feb 202019 Feb 2020

Publication series

NameProceedings of SPIE
Volume11314
ISSN (Print)1605-7422

Conference

ConferenceMedical Imaging 2020: Computer-Aided Diagnosis
CountryUnited States
CityHouston
Period16/02/2019/02/20

Keywords

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

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    Heslinga, F. G., Pluim, J. P. W., Houben, A. J. H. M., Schram, M. T., Henry, R. M. A., Stehouwer, C. D. A., Van Greevenbroek, M. J., Berendschot, T. T. J. M., & Veta, M. (2020). Direct classification of type 2 diabetes from retinal fundus images in a population-based sample from the Maastricht study. In H. K. Hahn, & M. A. Mazurowski (Eds.), Medical Imaging 2020: Computer-Aided Diagnosis [113141N] (Proceedings of SPIE; Vol. 11314). SPIE. https://doi.org/10.1117/12.2549574