Method for performing classification of the severity of at least one liver disease from non-invasive radiographic images acquired during a scan, the method comprising: - providing (100, 110) radiographic images of slices of at least the abdomen of a patient, with at least two radiographic images being provided for each slice; and - pre-processing (200, 210) said radiographic images at least by: o for each slice, segmenting (202, 212) liver and spleen from at least one of said at least two radiographic images, thus achieving a spleen binary mask and a liver binary mask per slice, and o normalizing (204, 214) said radiographic images with each other, thus achieving at least two normalized radiographic images per slice; the method further comprising: - for each slice, at least from the liver binary mask and said at least two normalized radiographic images, extracting (300, 310) at least one parameter characterizing the liver; - from at least one spleen binary mask, extracting (400, 410) at least one parameter characterizing the spleen; and - inputting (500, 510) said at least one parameter characterizing the liver and said at least one parameter characterizing the spleen into a trained Machine Learning, ML, model, with this latter being designed to classify the severity of the at least one liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage in function of both of said at least one parameter characterizing the liver and said at least one parameter characterizing the spleen, the method being characterized in that said at least two radiographic images comprise at least: - a radiographic image, called MRI image, acquired by magnetic resonance imaging, MRI, and - a radiographic image, called MRE image, acquired by magnetic resonance elastography, MRE. - providing (100, 110) radiographic images of slices of at least the abdomen of a patient, with at least two radiographic images being provided for each slice; and - pre-processing (200, 210) said radiographic images at least by: o for each slice, segmenting (202, 212) liver and spleen from at least one of said at least two radiographic images, thus achieving a spleen binary mask and a liver binary mask per slice, and o normalizing (204, 214) said radiographic images with each other, thus achieving at least two normalized radiographic images per slice; o for each slice, segmenting (202, 212) liver and spleen from at least one of said at least two radiographic images, thus achieving a spleen binary mask and a liver binary mask per slice, and o normalizing (204, 214) said radiographic images with each other, thus achieving at least two normalized radiographic images per slice; the method further comprising: - for each slice, at least from the liver binary mask and said at least two normalized radiographic images, extracting (300, 310) at least one parameter characterizing the liver; - from at least one spleen binary mask, extracting (400, 410) at least one parameter characterizing the spleen; and - inputting (500, 510) said at least one parameter characterizing the liver and said at least one parameter characterizing the spleen into a trained Machine Learning, ML, model, with this latter being designed to classify the severity of the at least one liver disease between one among a group of liver disease at early stage and a group of liver disease at advanced stage in function of both of said at least one parameter characterizing the liver and said at least one parameter characterizing the spleen, - for each slice, at least from the liver binary mask and said at least two normalized radiographic images, extracting (300, 310) at least one parameter characterizing the liver; - from at least one spleen binary mask, extracting (400, 410) at least one parameter characterizing the spleen; and - inputting (500, 510) said at least one parameter characterizing the liver and said at least one parameter characterizing the spleen into a trained Machine Learning, ML, model, the method being characterized in that said at least two radiographic images comprise at least: - a radiographic image, called MRI image, acquired by magnetic resonance imaging, MRI, and - a radiographic image, called MRE image, acquired by magnetic resonance elastography, MRE. - a radiographic image, called MRI image, acquired by magnetic resonance imaging, MRI, and - a radiographic image, called MRE image, acquired by magnetic resonance elastography, MRE.