Abstract:

In view of the massive complex and high dimensional stone image data and the uncertain, fragmented and heterogeneous stone information caused by different imaging techniques, deep learning is an efficient big data processing tool with excellent feature extraction and nonlinear recognition capabilities, and has become an intelligent solution to improve the level of kidney stone management. The application of deep learning in kidney stone image data analysis can adapt to the characteristics of different types of stone data, and use multi-variable comprehensive assessment of stones to achieve accurate quantification, improve the efficiency and accuracy of diagnosis and evaluation, and assist doctors to develop better stone treatment plan. This study reviews the progress of deep learning in kidney stone imaging in recent years, focuses on various deep learning algorithms, illustrates their novelty and advantages over traditional methods and machine learning algorithms, and points out their shortcomings, aiming to provide reference and direction for subsequent research work.

Key words: kidney stones, stone analysis, stone diagnosis, stone classification, computer science, deep learning, machine learning, medical big data, intelligent healthcare