A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity

Giuseppe Maulucci, Marco De Spirito, Dario Pitocco, Alessandro Rizzi, Ermanno Cordelli, Paolo Soda

Research output: Contribution to journalArticlepeer-review

14 Citations (Scopus)

Abstract

Background and objective: Investigation of membrane fluidity by metabolic functional imaging opens up a new and important area of translational research in type 1 diabetes mellitus, being a useful and sensitive biomarker for disease monitoring and treatment. We investigate here how data on membrane fluidity can be used for diabetes monitoring. Methods: We present a decision support system that distinguishes between healthy subjects, type 1 diabetes mellitus patients, and type 1 diabetes mellitus patients with complications. It leverages on dual channel data computed from the physical state of human red blood cells membranes by means of features based on first- and second-order statistical measures as well as on rotation invariant co-occurrence local binary patterns. The experiments were carried out on a dataset of more than 1000 images belonging to 27 subjects. Results: Our method shows a global accuracy of 100%, outperforming also the state-of-the-art approach based on the glycosylated hemoglobin. Conclusions: The proposed recognition approach permits to achieve promising results.
Original languageEnglish
Pages (from-to)263-271
Number of pages9
JournalComputer Methods and Programs in Biomedicine
Volume162
DOIs
Publication statusPublished - 2018

Keywords

  • Case-Control Studies
  • Computer Science Applications1707 Computer Vision and Pattern Recognition
  • Diabetes Mellitus, Type 1
  • Diagnosis, Computer-Assisted
  • Erythrocyte Membrane
  • Erythrocytes
  • Feature extraction
  • Female
  • Glycated Hemoglobin A
  • Health Informatics
  • Humans
  • Image Processing, Computer-Assisted
  • Image processing
  • Machine learning
  • Male
  • Membrane Fluidity
  • Pattern Recognition, Automated
  • Reproducibility of Results
  • Software
  • Two-photon microscopy
  • Type 1 Diabetes

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