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

Onderzoeksoutput: Hoofdstuk in Boek/Rapport/CongresprocedureConferentiebijdrageAcademicpeer review

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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.

Originele taal-2Engels
TitelMedical Imaging 2020
SubtitelComputer-Aided Diagnosis
RedacteurenHorst K. Hahn, Maciej A. Mazurowski
ISBN van elektronische versie9781510633957
StatusGepubliceerd - 1 jan 2020
EvenementMedical Imaging 2020: Computer-Aided Diagnosis - Houston, Verenigde Staten van Amerika
Duur: 16 feb 202019 feb 2020

Publicatie series

NaamProceedings of SPIE
ISSN van geprinte versie1605-7422


CongresMedical Imaging 2020: Computer-Aided Diagnosis
LandVerenigde Staten van Amerika

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