Purpose: Foley catheter (FC) insertion is very basic yet one of the most widely performed procedures all across the fields of medicine. Since FC was first introduced in 19020’s, no significant improvement has been made in view of methodology, despite the inconvenience associated with cumbersome preparation, procedure, and the patients’ discomfort with having to have their genitalia exposed. We developed a new, easy-to-use FC insertion device, Quick Foley, that provides an innovative approach to introducing FC while simplifying and minimizing time spent without compromising the sterility. Methods: We developed an all-in-one disposable FC introducer contains all the necessary components in a single-device-kit. Minimal plastic components are necessary to keep accuracy and consistency, but the rest are made of the paper to minimize plastic waste. The preparation is done by connecting to the drainage bag, spurring the lubricant gel through gel insert, separating the tract, and connecting with the ballooning syringe. For the insertion, after sterilizing the urethral orifice, rotate the control knob to feed FC to the end of the urethra. After ballooning, dissembling of the device is done only by opening and removing the module, then only the FC remains. Results: As the device is all-in-one, there is no need to prearrange the FC tray, simplifies the FC preparation and catheterization procedure. This device not only makes it convenient for the practitioner, but ultimately, it will reduce the psychological discomfort experienced by patient by truncating perineal exposure time. Conclusions We have successfully developed a novel device that reduces the cost and burden of using FC for practitioners while maintaining an aseptic technique. Furthermore, this all-in-one device allows the entire procedure to be completed much more quickly compared to the current method, so this minimizes perineal exposure time. Both practitioners and patients can benefit by this new device.

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.

Purpose: In this paper, we present the development of a monitoring system designed to aid in the management and prevention of conditions related to urination. The system features an artificial intelligence (AI)-based recognition technology that automatically records a user’s urination activity. Additionally, we developed a technology that analyzes movements to prevent neurogenic bladder. Methods: Our approach included the creation of AI-based recognition technology that automatically logs users’ urination activities, as well as the development of technology that analyzes movements to prevent neurogenic bladder. Initially, we employed a recurrent neural network model for the urination activity recognition technology. For predicting the risk of neurogenic bladder, we utilized convolutional neural network (CNN)-based AI technology. Results: The performance of the proposed system was evaluated using a study population of 30 patients with urinary tract dysfunction, who collected data over a 60-day period. The results demonstrated an average accuracy of 94.2% in recognizing urinary tract activity, thereby confirming the effectiveness of the recognition technology. Furthermore, the motion analysis technology for preventing neurogenic bladder, which also employed CNN-based AI, showed promising results with an average accuracy of 83%. Conclusions In this study, we developed a urination disease monitoring system aimed at predicting and managing risks for patients with urination issues. The system is designed to support the entire care cycle of a patient by leveraging AI technology that processes various image and signal data. We anticipate that this system will evolve into digital treatment products, ultimately providing therapeutic benefits to patients.

Purpose: This paper aims to develop a clinical decision support system (CDSS) that can help detect the stone that is most important to the diagnosis of urolithiasis. Among them, especially for the development of artificial intelligence (AI) models that support a final judgment in CDSS, we would like to study the optimal AI model by comparing and evaluating them. Methods: This paper proposes the optimal ureter stone detection model using various AI technologies. The use of AI technology compares and evaluates methods such as machine learning (support vector machine), deep learning (ResNet-50, Fast R-CNN), and image processing (watershed) to find a more effective method for detecting ureter stones. Results: The final value of sensitivity, which is calculated using true positive (TP) and false negative and is a measure of the probability of TP results, showed high recognition accuracy, with an average value of 0.93 for ResNet-50. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery. Conclusions The general situation in the most effective way to the detection stone can be found. But a variety of variables may be slightly different the difference through the term could tell. Future works, on urological diseases, are diverse and the research will be expanded by customizing AI models specialized for those diseases.