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Published on: February 28, 2013
A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell
Ermanno Cordelli1, Giuseppe Maulucci2, Marco De Spirito2
1Unit of Computer Systems and Bioinformatics, Department of Engineering, Università Campus Bio-Medico di Roma, Rome, Italy.
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.
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