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Quantifying the Impact of Type 2 Diabetes on Brain Perfusion Using Deep Neural Networks.

Behrouz Saghafi1, Prabhat Garg1, Benjamin C Wagner1

  • 1University of Texas Southwestern Medical Center, Dallas, TX, USA.

Deep Learning in Medical Image Analysis and Multimodal Learning for Clinical Decision Support : Third International Workshop, DLMIA 2017, and 7Th International Workshop, ML-CDS 2017, Held in Conjunction with MICCAI 2017 Quebec City, QC
|October 26, 2019
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Summary

Type 2 Diabetes (T2D) impacts brain health, specifically cerebral blood flow. A novel deep neural network accurately identified low or high perfusion using clinical data, outperforming other models.

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Area of Science:

  • Neuroscience
  • Medical Imaging
  • Artificial Intelligence

Background:

  • Type 2 Diabetes (T2D) is a prevalent metabolic disorder with known detrimental effects on renal and cardiovascular systems.
  • Previous studies indicate T2D significantly alters brain structure, including shape, volume, and white matter integrity.
  • The specific impact of T2D on brain perfusion remains incompletely understood.

Purpose of the Study:

  • To quantify the association between Type 2 Diabetes and regional Cerebral Blood Flow (CBF).
  • To develop and evaluate a deep neural network for classifying CBF levels based on clinical measures.
  • To explore nonlinear relationships between clinical predictors and brain perfusion in T2D patients.

Main Methods:

  • A fully-connected deep neural network was proposed to classify regional CBF as low or high.
  • The model utilized 16 clinical measures, including diabetes, renal, cardiovascular, and demographic data, as input features.
  • The end-to-end architecture automatically learned relevant features without manual selection.

Main Results:

  • The proposed deep neural network achieved promising classification performance in identifying regional CBF levels.
  • The model demonstrated the ability to uncover nonlinear associations between clinical measures and brain perfusion.
  • Comparative analysis showed the proposed model outperformed six classical machine learning algorithms and six alternative deep neural networks.

Conclusions:

  • Deep neural networks offer a powerful approach to understanding the complex relationship between Type 2 Diabetes and brain perfusion.
  • The developed model provides a novel tool for assessing T2D-related changes in cerebral blood flow.
  • This research highlights the potential of AI in neuroimaging for metabolic disease research.