TY - JOUR
T1 - A decision support system for type 1 diabetes mellitus diagnostics based on dual channel analysis of red blood cell membrane fluidity
AU - Cordelli, Ermanno
AU - Maulucci, Giuseppe
AU - De Spirito, Marco
AU - Rizzi, Alessandro
AU - Pitocco, Dario
AU - Soda, Paolo
PY - 2018
Y1 - 2018
N2 - 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.
AB - 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.
KW - Case-Control Studies
KW - Computer Science Applications1707 Computer Vision and Pattern Recognition
KW - Diabetes Mellitus, Type 1
KW - Diagnosis, Computer-Assisted
KW - Erythrocyte Membrane
KW - Erythrocytes
KW - Feature extraction
KW - Female
KW - Glycated Hemoglobin A
KW - Health Informatics
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Image processing
KW - Machine learning
KW - Male
KW - Membrane Fluidity
KW - Pattern Recognition, Automated
KW - Reproducibility of Results
KW - Software
KW - Two-photon microscopy
KW - Type 1 Diabetes
KW - Case-Control Studies
KW - Computer Science Applications1707 Computer Vision and Pattern Recognition
KW - Diabetes Mellitus, Type 1
KW - Diagnosis, Computer-Assisted
KW - Erythrocyte Membrane
KW - Erythrocytes
KW - Feature extraction
KW - Female
KW - Glycated Hemoglobin A
KW - Health Informatics
KW - Humans
KW - Image Processing, Computer-Assisted
KW - Image processing
KW - Machine learning
KW - Male
KW - Membrane Fluidity
KW - Pattern Recognition, Automated
KW - Reproducibility of Results
KW - Software
KW - Two-photon microscopy
KW - Type 1 Diabetes
UR - http://hdl.handle.net/10807/132440
UR - http://www.elsevier.com/locate/cmpb
U2 - 10.1016/j.cmpb.2018.05.025
DO - 10.1016/j.cmpb.2018.05.025
M3 - Article
SN - 0169-2607
VL - 162
SP - 263
EP - 271
JO - Computer Methods and Programs in Biomedicine
JF - Computer Methods and Programs in Biomedicine
ER -