Urinary stones can cause lateral abdominal pain, such as side pain, and present in an intermittent pattern, temporarily subsiding before reappearing. In men, the pain may radiate to the lower abdomen, testicle, and scrotum, while in women, it may extend to the vagina. When stones are located near the bladder, symptoms of bladder irritation, such as frequent urination, may also occur. Severe pain can be accompanied by nausea, vomiting, and abdominal swelling, and urinary stones may also cause hematuria [1-3]. The primary cause of urinary tract stones is reduced water intake. This decrease leads to urinary crystals remaining in the urine for extended periods, increasing the likelihood of stone formation. Urinary tract stones are more common in men, occurring at a rate three times higher than in women, and are often seen in individuals in their 20s to 40s. The prevalence of this condition is particularly high in mountainous, desert, and tropical regions, suggesting that diet, temperature, and humidity may contribute to its development. Temperature and season are significant factors in the formation of urinary tract stones; during summer, concentrated urine from excessive sweating can facilitate stone production. Additionally, excessive sun exposure can lead to an overproduction of vitamin D, and a high intake of animal protein can increase the risk of urinary stones by raising the excretion of secondary calcium, phosphates, and uric acid [4,5].

Urinary tract stones can be diagnosed through the identification of the patient’s symptoms, physical examination, urinalysis, or radiographic imaging. Patients with suspected ureteral stones often experience severe pain in the back, and when a urinary tract infection is present, urinalysis may reveal an elevated white blood cell count in the urine. It is important to note that while a simple radiographic scan can confirm the presence of a stone, it cannot diagnose stones that are not radiopaque. Furthermore, ureteral stones may be obscured by pelvic bones or masked by fecal matter or other organs, complicating their detection. Therefore, to ascertain the presence of ureteral stones, hospitals may utilize angiography or computed tomography (CT) [6].

In the field of deep learning vision, high-performing technologies such as the convolutional neural network (CNN) [7] and transfer learning have been developed, along with the vision transformer (ViT) model [8], which has set new state-of-the-art records in many areas. Numerous studies employing artificial intelligence have been conducted in the medical and healthcare fields. Research has been carried out to detect kidney or urinary stones [9-12], manage urinary tract stones [13], predict the risk of urinary tract stones [14], and enhance the resolution of CT images using CNN [15]. However, the ViT model tends to underperform when fine-tuning with limited datasets. Therefore, in this paper, we propose a pipeline that utilizes medical data to determine the presence or absence of urinary tract stones with the ViT model.

In this study, a total of 150 images were utilized, comprising 100 images of urinary stones and 50 images not containing urinary stones. These images were obtained from patients who visited Sejong Chungnam University Hospital and were verified by medical experts. The collected data were anonymized and, for research purposes, stored on a separate storage device in DICOM format. Fig. 1 represents the CT scans used in the research.

For stone detection, the ViT backbone model was utilized, necessitating the resizing of input images to 224×224 dimensions. A notable feature of the ViT model is its capacity to achieve outstanding performance when fine-tuned with high-resolution images, irrespective of the resolution of pre-trained images. To facilitate this, the technique of histogram smoothing [16] is applied. Histogram smoothing modifies the pixel value distribution within the image to create a more uniform distribution, which in turn enhances image contrast and improves performance.

Furthermore, extensive research has been conducted in the field of medical CT imaging, involving super-resolution techniques to enhance image resolution. Building on this research, the super-resolution CNN (SRCNN) [17] was employed in this experiment to increase the resolution of the data used. SRCNN is a technology that upscales low-resolution images to high resolution, restoring fine details and enhancing the overall image resolution. This improvement in data quality contributes to the enhancement of the model’s performance. The SRCNN employs bicubic interpolation to upscale a single low-resolution image to the desired size. It transforms the target low-resolution image, denoted as Y, into F(Y), with the goal of identifying a mapping function F that enables F(Y) to closely approximate the original high-resolution image X. The process adheres to the following steps:

F₁(Y)=max(0,W₁×Y+B₁) (1)

F₁(Y)=max(0,W₂×F₁(Y)+B₂) (2)

F(Y)=W₃×F₂(Y)+B₃ (3)

First, patch extraction and representation: This process involves extracting patches from the low-resolution image Wai using the first layer of a CNN and representing each patch as a high-level vector [1]. Next, nonlinear mapping: The high-dimensional vectors generated in the process [1] are nonlinearly mapped to other high-dimensional vectors. Finally, reconstruction: The high-resolution representations produced through the aforementioned processes are amalgamated to construct the final high-resolution image. At this stage, the resulting value should closely approximate the actual correct answer, X.

A pipeline for detecting the presence and absence of urinary stones was developed using a ViT model. To evaluate the model’s performance, accuracy, precision, and recall were used as indicators. Additionally, to ensure the model’s generalizability, 5-fold cross-validation was employed. Table 1 presents the results of the cross-validation comparison among different deep learning and machine learning models.

Urinary tract stones cause lateral pain and various symptoms, and they occur with particularly high frequency in men and in young people in their 20s to 40s. The primary cause is an increase in urine concentration due to insufficient water intake. Additionally, environmental factors play a role, particularly in highland, desert, and tropical regions. Given these factors, the absence of urinary tract stones is regarded as a significant medical issue. The diagnosis of urinary tract stones may involve evaluating the patient’s symptoms, conducting a physical examination, performing a urine test, or utilizing imaging techniques. However, standard radiography may not always detect all stones, occasionally necessitating more advanced imaging methods such as angiography or CT scans.

This study introduces a pipeline designed to leverage medical data for the diagnosis of urinary tract stones. The dataset employed in this research comprised 150 CT images sourced from Sejong Chungnam National University Hospital. These included 100 images from patients diagnosed with urinary tract stones and 50 images from patients without urinary tract stones.

In the data preprocessing phase, the SRCNN model was utilized to enhance image resolution. This preprocessing step enhances the model’s performance and facilitates learning that takes into account the spatial structure.

Data augmentation plays an important role in improving the performance of the model, particularly through the application of technologies like CycleGAN to increase data diversity. Such improvements bolster the models’ generalization capabilities, allowing them to leverage a wide array of data styles.

The ViT model learns spatial structure by segmenting images into patches and transforming them into vectors, thereby enabling efficient learning while preserving the images’ immutability. Utilizing this model, we developed a system for detecting urinary tract stones, which demonstrated effective performance in our experimental results.

A 5-fold cross-verification method was used to evaluate the model’s performance, and accuracy, precision and recall scores were used as evaluation indicators. The results yielded a precision of 0.9396, a recall of 0.9476, and an accuracy of 0.93. These outcomes are anticipated to validate the model’s diagnostic capabilities and aid in the early diagnosis and treatment of patients with urinary tract stones.