Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

Anastasiadis, Anastasios; Koudonas, Antonios; Langas, Georgios; Tsiakaras, Stavros; Memmos, Dimitrios; Mykoniatis, Ioannis; Symeonidis, Evangelos N.; Tsiptsios, Dimitrios; Savvides, Eliophotos; Vakalopoulos, Ioannis; Dimitriadis, G.; Rosette, Jean de la

doi:10.1016/j.ajur.2023.02.002

Cited by 7 publications

(1 citation statement)

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“…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][10][11][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.…”

Section: Injmentioning

confidence: 99%

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

Choi,

Kim,

Whangbo

et al. 2023

Int Neurourol J

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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.

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