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

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.

Transfer Learning for Effective Urolithiasis Detection / 대한배뇨장애요실금학회지

Purpose@#Urolithiasis is a common disease that can cause acute pain and complications. The objective of this study was to develop a deep learning model utilizing transfer learning for the rapid and accurate detection of urinary tract stones. By employing this method, we aim to improve the efficiency of medical staff and contribute to the progress of deep learning-based medical image diagnostic technology. @*Methods@#The ResNet50 model was employed to develop feature extractors for detecting urinary tract stones. Transfer learning was applied by utilizing the weights of pretrained models as initial values, and the models were fine-tuned with the provided data. The model’s performance was evaluated using accuracy, precision-recall, and receiver operating characteristic curve metrics. @*Results@#The ResNet-50-based deep learning model demonstrated high accuracy and sensitivity, outperforming traditional methods. Specifically, it enabled a rapid diagnosis of the presence or absence of urinary tract stones, thereby assisting doctors in their decision-making process. @*Conclusions@#This research makes a meaningful contribution by accelerating the clinical implementation of urinary tract stone detection technology utilizing ResNet-50. The deep learning model can swiftly identify the presence or absence of urinary tract stones, thereby enhancing the efficiency of medical staff. We expect that this study will contribute to the advancement of medical imaging diagnostic technology based on deep learning.

A Clinical Observation on Staghorn Calculi of the Kidney / 대한비뇨기과학회지

There were 20 patients (22 kidneys) with staghorn calculi evaluated clinically (Clinical presentation, Complications, Management) and kidneys were studied on pathologic basis. Only 15 percent of the patients could be defined as having a silent stone calculus. Clinical complication occurred in 50 percent of the patients. On 7 kidneys submitted for a pathoanatomic study (nephrectomy) hydropy0nephrosis was found in 28.6 percent, end stage pyelonephritic kidney in 28.6 percent, end stage hydronephOsis 14.2 percent, severe pyelonephritis 28.6 percent. The kidney was considered to be relatively undamaged in 22.7 percent, of the cases. Complete removal of the calculus and appropriate medical adjunctive therapy should be done early in the course of the disease in an attempt to prevent complications and renal deterioration. The results of treatment are discussed and compared to those obtained in a group of patients who initially were managed conservatively.

Torsion of Spermatic Cord / 대한비뇨기과학회지

Authors experienced two cases of torsion of spermatic cord recently and reviewed the literatures. The patients were 12 and 20 years old with chief complaints of sudden onset on left scrotal swelling and testicular pain. Physical examination was not significant except positive Prehn's sign on the involving side of testicle. Under the diagnosis. of torsion of spermatic cord. One was performed of left orchiectomy and the opposite side orchiopexy. The other man was performed of both orchiopexy. We reported 2 cases with review of the literature.