Gold standard methods for anaemia diagnosis are the complete blood count and the peripheral smear observation. However, they do not allow for a complete differential diagnosis, which requires biochemical assays, thus being labeldependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even by morphometric analysis as well as QPI. Here we exploit in-flow tomographic phase microscopy to retrieve the exact 3D rendering of Red Blood Cells (RBCs) from anaemic patients and to identify the pathology, distinguishing it from healthy samples. Moreover, we introduce a Label-free Optical Marker (LOM) to detect RBC phenotypes demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to the erythrocytes disease by modelling each RBC as a biolens. We tested this novel bio-photonic analysis by proofing that several inherited anaemias, specifically Iron-deficiency Anaemia, Thalassemia, Hereditary Spherocytosis and Congenital Dyserythropoietic Anaemia, can be identified and sorted thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.

Anaemias diagnosis by label-free quantitative phase imaging

Mugnano M;Memmolo P;Miccio L;Merola F;Bianco V;Ferraro P
2019

Abstract

Gold standard methods for anaemia diagnosis are the complete blood count and the peripheral smear observation. However, they do not allow for a complete differential diagnosis, which requires biochemical assays, thus being labeldependent techniques. On the other hand, recent studies focus on label-free quantitative phase imaging (QPI) of blood samples to investigate blood diseases by using video-based morphological methods. However, when sick cells are very similar to healthy ones in terms of morphometric features, identification of a blood disease becomes challenging even by morphometric analysis as well as QPI. Here we exploit in-flow tomographic phase microscopy to retrieve the exact 3D rendering of Red Blood Cells (RBCs) from anaemic patients and to identify the pathology, distinguishing it from healthy samples. Moreover, we introduce a Label-free Optical Marker (LOM) to detect RBC phenotypes demonstrating that a single set of all-optical parameters can clearly identify a signature directly related to the erythrocytes disease by modelling each RBC as a biolens. We tested this novel bio-photonic analysis by proofing that several inherited anaemias, specifically Iron-deficiency Anaemia, Thalassemia, Hereditary Spherocytosis and Congenital Dyserythropoietic Anaemia, can be identified and sorted thus opening a novel route for blood diagnosis on a completely different concept based on LOMs.
2019
blood testing;
anemias;
diagnostics;
imaging;
digital holography;
machine learning;
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/20.500.14243/379501
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