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

• Published in: Computer methods and programs in biomedicine Vol. 162; pp. 263 - 271
• Main Authors: Cordelli, Ermanno, Maulucci, Giuseppe, De Spirito, Marco, Rizzi, Alessandro, Pitocco, Dario, Soda, Paolo
• Format: Journal Article
• Language: English
• Published: Ireland Elsevier B.V 01.08.2018
• Subjects: Feature extraction; Image processing; Machine learning; Two-photon microscopy; Type 1 Diabetes; Type 1 Diabetes; Two-photon microscopy; Feature extraction; Image processing; Machine learning
• ISSN: 0169-2607; 1872-7565; 1872-7565
• DOI: 10.1016/j.cmpb.2018.05.025

•Investigate the use of human cells membrane fluidity for type 1 diabetes monitoring.•We present a decision support system that classifies type 1 diabetes mellitus patients.•The experiments were carried out on a wide dataset of images from the subjects.•The results outperform the glycosylated hemoglobin test used in the state-of-the-art. 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.