Generative models for predicting chemical composition of gallstones

Abstract

Objective: As we know, gallstones are a gallbladder disease with high incidence around the world. As the population has aged and living habits have changed, the incidence of the disease is increasing year by year. Gallstones are mainly classified into cholesterol, bile pigment and mixed type gallstones based on their chemical composition. Patients with different stone components have different treatment options. Therefore, it is very important to know the chemical type of the stone before treatment. Imaging examination is the main method to identify the components of gallstones in the body.

Materials and methods: Deep learning technology has an excellent data mining ability, and thus the combination of deep learning and medical treatment is always a research focus. In this work, we introduce a generative model to learn the features of the training data, to detect the composition of gallstones and to assist medical diagnosis. Furthermore, the theoretical analysis is given in detail.

Results: The model could be used to determine the chemical composition of gallstones.

Conclusions: The potential of generative models in predicting the chemical composition of gallstones is shown in this study. In addition, theoretical analysis is also presented.