An intelligent vital microscopy, IVM, device comprising: a receiver configured to receive at least one IVM image of a human microcirculation, MC, of an organ surface; a learning processor coupled to the receiver and configured to: process the at least one IVM image that visualises cellular constituents of the MC, wherein the MC comprises arterioles, capillaries and venules, representing blood vessels, their flowing cells including the fluid in which they are in, and tissue cells surrounding the blood vessels of the at least one IVM image and extract at least one MC variable therefrom, wherein the extracted at least one MC variable comprises at least one quantitative functional parameter of the MC that comprises at least one of: tissue red blood cell perfusion, tRBCp; total vessel density, TVD; blood volume; a proportion of perfused vessel density, PVD; a vessel diameter, VD; a RBC velocity; a proportion of perfused vessels, PPV; oxygen saturation of haemoglobin in red blood cells; a capillary tube and discharge haematocrit; and identify from the extracted at least one MC variable of the at least one IVM image, based on MC analysis of training data images repeatedly and previously input to the learning processor for analyzing a function and morphology of MC, at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen; and an output coupled to the learning processor and configured to output the identification of the at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen. a receiver configured to receive at least one IVM image of a human microcirculation, MC, of an organ surface; a learning processor coupled to the receiver and configured to: process the at least one IVM image that visualises cellular constituents of the MC, wherein the MC comprises arterioles, capillaries and venules, representing blood vessels, their flowing cells including the fluid in which they are in, and tissue cells surrounding the blood vessels of the at least one IVM image and extract at least one MC variable therefrom, wherein the extracted at least one MC variable comprises at least one quantitative functional parameter of the MC that comprises at least one of: tissue red blood cell perfusion, tRBCp; total vessel density, TVD; blood volume; a proportion of perfused vessel density, PVD; a vessel diameter, VD; a RBC velocity; a proportion of perfused vessels, PPV; oxygen saturation of haemoglobin in red blood cells; a capillary tube and discharge haematocrit; and identify from the extracted at least one MC variable of the at least one IVM image, based on MC analysis of training data images repeatedly and previously input to the learning processor for analyzing a function and morphology of MC, at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen; and an output coupled to the learning processor and configured to output the identification of the at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen. process the at least one IVM image that visualises cellular constituents of the MC, wherein the MC comprises arterioles, capillaries and venules, representing blood vessels, their flowing cells including the fluid in which they are in, and tissue cells surrounding the blood vessels of the at least one IVM image and extract at least one MC variable therefrom, wherein the extracted at least one MC variable comprises at least one quantitative functional parameter of the MC that comprises at least one of: tissue red blood cell perfusion, tRBCp; total vessel density, TVD; blood volume; a proportion of perfused vessel density, PVD; a vessel diameter, VD; a RBC velocity; a proportion of perfused vessels, PPV; oxygen saturation of haemoglobin in red blood cells; a capillary tube and discharge haematocrit; and identify from the extracted at least one MC variable of the at least one IVM image, based on MC analysis of training data images repeatedly and previously input to the learning processor for analyzing a function and morphology of MC, at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen; and an output coupled to the learning processor and configured to output the identification of the at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen. tissue red blood cell perfusion, tRBCp; total vessel density, TVD; blood volume; a proportion of perfused vessel density, PVD; a vessel diameter, VD; a RBC velocity; a proportion of perfused vessels, PPV; oxygen saturation of haemoglobin in red blood cells; a capillary tube and discharge haematocrit; and identify from the extracted at least one MC variable of the at least one IVM image, based on MC analysis of training data images repeatedly and previously input to the learning processor for analyzing a function and morphology of MC, at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen; and an output coupled to the learning processor and configured to output the identification of the at least one of: an underlying cause for an observed abnormality, an intervention, a disease state, a disease diagnosis, a presence of a pathogen.