Abstract: The present disclosure, in some embodiments, relates to a non-transitory computer-readable medium storing computer-executable instructions that, when executed, cause a processor to perform operations, including accessing an imaging data set having one or more radiological images of a patient having Crohn's disease, the one or more radiological images including one or more intestinal strictures; identifying a plurality of determinative features from within the one or more intestinal strictures in the one or more radiological images, the plurality of determinative features being associated with one or more pathological features used to identify inflammation or fibrosis within an intestinal stricture; and applying a machine learning model to the plurality of determinative features to identify an extent of inflammation or fibrosis within the one or more intestinal strictures.

Abstract: By providing projections extending outwardly from the base of a wet tamping face of a tamping labeler, which projections maintain at least a substantial portion of a label at a stand off from said base, the surface tension between the label and tamping face is reduced. This reduction in surface tension helps ensure that when a label is tamped against a product, it remains there. The tamping face may be provided with a regular, or irregular, pattern of projections, or with a texture that results in the projections.

Abstract: A product labelling apparatus has a plurality of labellers, an imager for imaging products; and a processor responsive to an output of the imager and operatively connected to a control input of each of the labellers. The processor processes an image received from the imager to identify a portion of a product which portion will pass a target area of a given labeller. The processor then tracks progress of that portion of the product and controls an appropriate one of the labellers to label the portion of the product when that portion of the product is at the target area of the given labeller.