Investigation of membrane fluidity by two photon fluorescence microscopy opens up a new and important area of translational research, being a useful and sensitive method for disease monitoring and treatment. In this paper we investigate if biomembranes in human red blood cells (RBC) and peripheral mononuclear cells (PMC) could be used as markers for type 1 diabetes mellitus (T1DM) diagnosis, leading to the development of a method for monitoring T1DM progression that nowadays is lacking, as clinical exams cannot pursue this task with enough reliability. To this aim, we present a set of features computed from PMC and RBC images that are given to a multi-experts system leveraging on multi-spectral information for positive/negative classifications. The experiments are carried out on a dataset of 800 blood cell images belonging to 18 subjects adopting the leave-one-person-out approach.
Cordelli, E., Pani, G., Pitocco, D., Maulucci, G., Soda, P., Early experiences in using blood cells biomembranes as markers for diabetes diagnosis, Paper (Trinity College Health Sciences Building at St. James's Hospital, irl, 20-23 June 2016), <<PROCEEDINGS IEEE INTERNATIONAL SYMPOSIUM ON COMPUTER-BASED MEDICAL SYSTEMS>>, 2016; 2016- (2016): 197-202.[doi: 10.1109/CBMS.2016.60] [http://hdl.handle.net/10807/96164]
Early experiences in using blood cells biomembranes as markers for diabetes diagnosis
Pani, GiovambattistaSecondo
;Pitocco, Dario;Maulucci, GiuseppePenultimo
;
2016
Abstract
Investigation of membrane fluidity by two photon fluorescence microscopy opens up a new and important area of translational research, being a useful and sensitive method for disease monitoring and treatment. In this paper we investigate if biomembranes in human red blood cells (RBC) and peripheral mononuclear cells (PMC) could be used as markers for type 1 diabetes mellitus (T1DM) diagnosis, leading to the development of a method for monitoring T1DM progression that nowadays is lacking, as clinical exams cannot pursue this task with enough reliability. To this aim, we present a set of features computed from PMC and RBC images that are given to a multi-experts system leveraging on multi-spectral information for positive/negative classifications. The experiments are carried out on a dataset of 800 blood cell images belonging to 18 subjects adopting the leave-one-person-out approach.
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