Predicting Blood Glucose Levels with Organic Neuromorphic Micro-Networks

Ibrahim Kurt, Imke Krauhausen, Simone Spolaor, Yoeri van de Burgt (Corresponding author)

Research output: Contribution to journalArticleAcademicpeer-review


Accurate glucose prediction is vital for diabetes management. Artificial intelligence and artificial neural networks (ANNs) are showing promising results for reliable glucose predictions, offering timely warnings for glucose fluctuations. The translation of these software-based ANNs into dedicated computing hardware opens a route toward automated insulin delivery systems ultimately enhancing the quality of life for diabetic patients. ANNs are transforming this field, potentially leading to implantable smart prediction devices and ultimately to a fully artificial pancreas. However, this transition presents several challenges, including the need for specialized, compact, lightweight, and low-power hardware. Organic polymer-based electronics are a promising solution as they have the ability to implement the behavior of neural networks, operate at low voltage, and possess key attributes like flexibility, stretchability, and biocompatibility. Here, the study focuses on implementing software-based neural networks for glucose prediction into hardware systems. How to minimize network requirements, downscale the architecture, and integrate the neural network with electrochemical neuromorphic organic devices, meeting the strict demands of smart implants for in-body computation of glucose prediction is investigated.

Original languageEnglish
Article numbere2308261
JournalAdvanced Science
Issue numberX
Publication statusE-pub ahead of print - 29 Apr 2024


  • glucose prediction
  • hardware computing
  • in-body computation
  • neural networks
  • organic neuromorphic computing
  • wearable


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