Social networks constitute an almost endless source of social behavior information. In fact, sometimes the amount of information is so large that the task to extract meaningful information becomes impossible due to temporal constrictions. We developed an artificial-intelligence-based method that reduces the calculation time several orders of magnitude when conveniently trained. We exemplify the problem by extracting data freely available in a commonly used social network, Twitter, building up a complex network that describes the online activity patterns of society. These networks are composed of a huge number of nodes and an even larger number of connections, making extremely difficult to extract meaningful data that summarizes and/or describes behaviors. Each network is then rendered into an image and later analyzed using an AI method based on Convolutional Neural Networks to extract the structural information.

FastSAM, a publicly available image segmentation model designed for general image segmentation, is turned into a highly adaptable and advanced segmentation tool that requires minimal training in two distinct scenarios. In the first case, we examine macroscopic X-ray images of the knee, in the second case, we focus on microscopic images of the zebra fish embryo retina, which have a significantly smaller spatial scale. We determine the minimum number of images needed to maintain state-of-the-art segmentation quality in each case. Finally, we evaluate the impact of image filtering and the unique considerations of segmenting 3D retinal volumes.