Generation and evaluation of synthetic kidney stones images generated by diffusion models using limited data

One of the diseases that affects men and women around the world is kidney stones. Kidney stones are the accumulation of minerals inside the kidneys and can cause severe pain or problems with the urinary system. There are many different types of kidney stones and it is very important to identify and classify them to know which is the best treatment for it and to avoid relapses. Currently, there are very few specialists who can perform this analysis, since it is a complicated process that requires a lot of experience and the methods that are used currently to do this process can take a long time. Numerous studies have shown that deep learning methods hold great promise in automating the classification of kidney stones. These advanced algorithms leverage neural networks to analyze and interpret complex medical imaging data with high precision. By training on large datasets of annotated kidney stone images, deep learning models can learn to identify and classify different types of stones, such as calcium oxalate, uric acid, and struvite, with remarkable precision. Research has demonstrated that these models can achieve performance levels comparable to, and sometimes exceeding those of experienced radiologists. The ability of deep learning methods to process large amounts of data quickly and consistently makes them particularly valuable in clinical settings, where timely and accurate diagnosis is crucial. However, data scarcity represents a big challenge in using deep learning methods for kidney stone classification because Deep learning algorithms require large and diverse datasets to train effectively, capturing the wide variability in stone appearances and characteristics seen in different patients, but acquiring such extensive datasets in the medical field is difficult due to privacy concerns, the labor-intensive process of annotating medical images, and the relatively low prevalence of certain types of kidney stones. The objective of this study is to solve the problem of missing data through data augmentation using the SinDDM model, which is a diffusion model capable of generating synthetic images from a single training image. To evaluate the generated synthetic images, a case study was also carried out to compare the performance of a classifier when using generated images and when using only real images. The results obtained indicate an 11% improvement in the accuracy of the classifier, thus demonstrating that the proposed method is efficient in solving the data scarcity problem.

https://orcid.org/0000-0002-9896-8727