Quantifying the impact of type 2 diabetes on brain perfusion using deep neural networks

Behrouz Saghafi, Prabhat Garg, Benjamin C. Wagner, S. Carrie Smith, Jianzhao Xu, Ananth J. Madhuranthakam, Youngkyoo Jung, Jasmin Divers, Barry I. Freedman, Joseph A. Maldjian, Albert Montillo

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

The effect of Type 2 Diabetes (T2D) on brain health is poorly understood. This study aims to quantify the association between T2D and perfusion in the brain. T2D is a very common metabolic disorder that can cause long term damage to the renal and cardiovascular systems. Previous research has discovered the shape, volume and white matter microstructures in the brain to be significantly impacted by T2D. We propose a fully-connected deep neural network to classify the regional Cerebral Blood Flow into low or high levels, given 16 clinical measures as predictors. The clinical measures include diabetes, renal, cardiovascular and demographics measures. Our model enables us to discover any nonlinear association which might exist between the input features and target. Moreover, our end-to-end architecture automatically learns the most relevant features and combines them without the need for applying a feature selection method. We achieved promising classification performance. Furthermore, in comparison with six (6) classical machine learning algorithms and six (6) alternative deep neural networks similarly tuned for the task, our proposed model outperformed all of them.

Original languageEnglish (US)
Title of host publicationDeep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings
EditorsTal Arbel, M. Jorge Cardoso
PublisherSpringer-Verlag
Pages151-159
Number of pages9
ISBN (Print)9783319675572
DOIs
StatePublished - Jan 1 2017
Externally publishedYes
Event3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017 - Quebec City, Canada
Duration: Sep 14 2017Sep 14 2017

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10553 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Conference

Conference3rd International Workshop on Deep Learning in Medical Image Analysis, DLMIA 2017 and 7th International Workshop on Multimodal Learning for Clinical Decision Support, ML-CDS 2017 held in Conjunction with 20th International Conference on Medical Image Computing and Computer Assisted Intervention, MICCAI 2017
CountryCanada
CityQuebec City
Period9/14/179/14/17

ASJC Scopus subject areas

  • Theoretical Computer Science
  • Computer Science(all)

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  • Cite this

    Saghafi, B., Garg, P., Wagner, B. C., Smith, S. C., Xu, J., Madhuranthakam, A. J., Jung, Y., Divers, J., Freedman, B. I., Maldjian, J. A., & Montillo, A. (2017). Quantifying the impact of type 2 diabetes on brain perfusion using deep neural networks. In T. Arbel, & M. J. Cardoso (Eds.), Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support - 3rd International Workshop, DLMIA 2017 and 7th International Workshop, ML-CDS 2017 Held in Conjunction with MICCAI 2017, Proceedings (pp. 151-159). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 10553 LNCS). Springer-Verlag. https://doi.org/10.1007/978-3-319-67558-9_18