Device for realization a numerical image classifier to determine the visualization quality of endoscopic videocapsule images of a segment of the digestive tract, comprising: - a videocapsule allowing the acquisition of video of segments of the digestive tract; - means for storing the images, coupled to the videocapsule; - a database with images extracted from the videocapsule and classified during a ground-truth step: into images said with "adequate" visualization and into images said with "inadequate" visualization; according to a score, determined by a visual analysis of the images, based on one or more medical criteria, the medical criterion being chosen from the following list: the percentage of mucosa visualized, the brightness of the image, the presence of bubbles, the presence of bile/chyme, the presence of liquids and undigested debris, - means for processing and calculation connected to the means for storing and incorporating the database, and configured to: • calculate at least two numerical parameters, each relating to at least one of the medical criteria of the score, and extracting images from the database, • perform an automatic statistical learning according to the technique said "random forests" which presents: a) several sub-steps of random drawing with delivery of the same number of learning images, which is a subset of the images in the database; b) a sub-step of automatic realization of the classifier, with a succession of automatic thresholds applied to the calculated numerical parameters, by numerical analysis of these learning images to construct N binary decision trees, one binary decision tree per random draw, each binary decision tree being constructed using at least the two numerical parameters, the automatic thresholds being automatically calculated, at each node of each binary decision tree, with the numerical parameter which allows the distribution of the learning images into a first subgroup and into a second subgroup closest to the distribution of the images so-called "adequate" visualization and so-called "inadequate" visualization carried out during a "ground-truth" step, the resulting set of binary decision trees constituting the numerical classifier, the numerical classifier being determined to allocate automatically, at the end of sub-step b), the learning images into a first sub-group of learning images comprising the most "adequate" visualization images and a second sub-group of learning images comprising the most "inadequate" visualization images, wherein the numerical parameter(s) is/are selected from the following list: - a global colorimetric parameter of the images, - one or more parameters reflecting the abundance of bubbles in the images, - a parameter reflecting the brightness of the image which is the greyscale contrast of the images. - a videocapsule allowing the acquisition of video of segments of the digestive tract; - means for storing the images, coupled to the videocapsule; - a database with images extracted from the videocapsule and classified during a ground-truth step: into images said with "adequate" visualization and into images said with "inadequate" visualization; according to a score, determined by a visual analysis of the images, based on one or more medical criteria, the medical criterion being chosen from the following list: the percentage of mucosa visualized, the brightness of the image, the presence of bubbles, the presence of bile/chyme, the presence of liquids and undigested debris, - means for processing and calculation connected to the means for storing and incorporating the database, and configured to: • calculate at least two numerical parameters, each relating to at least one of the medical criteria of the score, and extracting images from the database, • perform an automatic statistical learning according to the technique said "random forests" which presents: a) several sub-steps of random drawing with delivery of the same number of learning images, which is a subset of the images in the database; b) a sub-step of automatic realization of the classifier, with a succession of automatic thresholds applied to the calculated numerical parameters, • calculate at least two numerical parameters, each relating to at least one of the medical criteria of the score, and extracting images from the database, • perform an automatic statistical learning according to the technique said "random forests" which presents: a) several sub-steps of random drawing with delivery of the same number of learning images, which is a subset of the images in the database; b) a sub-step of automatic realization of the classifier, with a succession of automatic thresholds applied to the calculated numerical parameters, a) several sub-steps of random drawing with delivery of the same number of learning images, which is a subset of the images in the database; b) a sub-step of automatic realization of the classifier, with a succession of automatic thresholds applied to the calculated numerical parameters, - a global colorimetric parameter of the images, - one or more parameters reflecting the abundance of bubbles in the images, - a parameter reflecting the brightness of the image which is the greyscale contrast of the images.