Determining the type of kidney stone is crucial to prescribe appropriate treatments and prevent recurrence. Currently, there are different approaches to identify the type of kidney stone; however, obtaining results through the reference ex vivo identification procedure can take several weeks, while in vivo visual recognition requires highly trained specialists. For this reason, machine learning models have been developed to provide urologists with an automated classification of kidney stones during ureteroscopy. Nevertheless, a common issue with these models is the lack of training data.

This methodology presents a deep learning method based on few-shot learning, aimed at producing sufficiently discriminative features for identifying kidney stone types in endoscopic images, even with a very limited number of samples. This approach was specifically designed for scenarios where endoscopic images are scarce or uncommon classes are present, enabling classification even with limited information. Additionally, the model was enhanced through a transfer learning approach and the use of few-shot learning-based methods. The results demonstrate that Prototypical Networks, using up to 25% of the training data, can achieve performance that is equal to or better than traditional deep learning models trained with 100% of the data.