Int Neurourol J.  2023 Nov;27(Suppl 2):S99-103. 10.5213/inj.2346292.146.

Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model

• Choi HS
• Kim JS
• Whangbo TK
• Eun SJ

• KMID: 2548214
• DOI: http://doi.org/10.5213/inj.2346292.146

Abstract

Purpose
Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones. However, due to insufficient datasets, the performance of these models has not reached a level suitable for practical application. Consequently, this study introduces a vision transformer (ViT)-based pipeline for detecting urinary stones, using computed tomography images with augmentation.
Methods
The super-resolution convolutional neural network (SRCNN) model was employed to enhance the resolution of a given dataset, followed by data augmentation using CycleGAN. Subsequently, the ViT model facilitated the detection and classification of urinary tract stones. The model’s performance was evaluated using accuracy, precision, and recall as metrics.
Results
The deep learning model based on ViT showed superior performance compared to other existing models. Furthermore, the performance increased with the size of the backbone model.
Conclusions
The study proposes a way to utilize medical data to improve the diagnosis of urinary tract stones. SRCNN was used for data preprocessing to enhance resolution, while CycleGAN was utilized for data augmentation. The ViT model was utilized for stone detection, and its performance was validated through metrics such as accuracy, sensitivity, specificity, and the F1 score. It is anticipated that this research will aid in the early diagnosis and treatment of urinary tract stones, thereby improving the efficiency of medical personnel.

Keyword

Deep learning; Ureteral calculi; Urolithiasis; Machine learning; Artificial intelligence