Back to Search Start Over

Deviation Maps for Understanding Thickness Changes of Inner Retinal Layers in Children with Type 1 Diabetes Mellitus

Authors :
Aline Götze
Heidrun Schumann
Oliver Stachs
Ruby Kala Prakasam
Dagmar-C. Fischer
Martin Röhlig
Anselm Jünemann
Publication Year :
2019
Publisher :
Taylor & Francis, 2019.

Abstract

Purpose: To analyze the use of deviation maps (DevMs) to understand thickness changes of inner retinal layers in optical coherence tomography (OCT) data. To test a new visual analytics (VA) method with reduced complexity of OCT data analysis by comparing the layer thickness of children with type 1 diabetes mellitus (T1DM) to matched controls. Methods: OCT was performed on unilateral eyes of 26 children with T1DM without diabetic retinopathy and 29 healthy children to obtain macular volume scans. Subsequently, segmented inner retinal layers were analyzed using VA. Deviation maps were generated to readily visualize thickness differences between both groups and to investigate thickness changes of individual patients in relation to the control group. Results: In DevMs of the patient group, the total retina (TR) demonstrated localized, irregular areas of thinning (mean ± standard deviation) involving foveal center, inner macula, and inferior-nasal outer macula (−9.31 ± 1.73 µm; p < 0.05). Similarly, retinal nerve fiber layer showed continuous and localized areas of thinning in both inner and outer macula, extending nasally (−5.45 ± 4.31 µm; p < 0.05). In DevMs of individual patients, the TR and inner retinal layers revealed remarkable changes in thickness that were present between patients at both late and early stages of diabetes. Conclusion: The VA method simplifies the in-depth analysis of OCT volume data from different groups and is effective in detecting retinal thickness changes in children with diabetes. It can be easily adopted in a clinical set-up and intuitively used in complex multidisciplinary studies.

Details

Database :
OpenAIRE
Accession number :
edsair.doi.dedup.....2ef752f4190753444a686a353b3a44e2
Full Text :
https://doi.org/10.6084/m9.figshare.7957448