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.

Background: Arsenic trioxide (ATO) is the standard treatment for relapsed acute promyelocytic leukemia (APL). However, consensus on post-remission therapies is still lacking. Methods: We evaluated 52 patients who experienced relapse following initial treatment of APL between 2000 and 2019 at Catholic Hematology Hospital. Among them, 41 patients received reinduction treatment, 30 with ATO-based regimen, whereas 11 with conventional intensive chemotherapy (IC). Results: The ATO reinduction group showed a significantly higher second molecular complete remission (mCR2) rate, superior neutrophil and platelet recovery, and a lower infection rate than the IC reinduction group. No significant differences were observed in survival outcomes after post-remission treatment among the ATO-based (N=19), autologous (N=12), and allogeneic (N=6) hematopoietic stem cell transplantation (HSCT) groups. In the ATO-based and autologous HSCT groups, among patients with mCR2 after ATO reinduction, nine and five patients experienced a second relapse, respectively (50.7% vs. 41.7%, P =0.878). Among these patients, seven received salvage allogeneic HSCT; six remained alive. The other seven patients received ATO without HSCT. Five died from disease progression, and two survived and have been in mCR2 since. Conclusion Post-remission treatment outcomes of patients with relapsed APL were not significantly different, regardless of the treatment option, suggesting the feasibility of ATO-based treatment without HSCT in mCR2. Allogeneic HSCT may be an effective salvage treatment modality for patients with a second relapse. Owing to a few cases of relapsed APL, multicenter prospective studies may help elucidate the efficacy of each post-remission treatment.

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.

The International Neurourology Journal (Int Neurourol J, INJ) is a quarterly international journal that publishes high-quality research papers that provide the most significant and promising achievements in the fields of clinical neurourology and fundamental science. Specifically, fundamental science includes the most influential research papers from all fields of science and technology, revolutionizing what physicians and researchers practicing the art of neurourology worldwide know. Thus, we welcome valuable basic research articles to introduce cutting-edge translational research of fundamental sciences to clinical neurourology. In the editorials, urologists will present their perspectives on these articles. The original mission statement of the INJ was published on October 12, 1997. INJ provides authors a fast review of their work and makes a decision in an average of 3 to 4 weeks of receiving submissions. If accepted, articles are posted online in fully citable form. Supplementary issues will be published interim to quarterlies, as necessary, to fully allow berth to accept and publish relevant articles. Science Citation Index Expanded (SCIE, Web of Science), Scopus, PubMed, PubMed Central, KoreaMed, KoMCI, WPRIM, WorldWideScience.org, DOI/Crossref, EBSCO, Google Scholar.

Purpose: This paper proposes a technological system that uses artificial intelligence to recognize and guide the operator to the exact stenosis area during endoscopic surgery in patients with urethral or ureteral strictures. The aim of this technological solution was to increase surgical efficiency. Methods: The proposed system utilizes the ResNet-50 algorithm, an artificial intelligence technology, and analyzes images entering the endoscope during surgery to detect the stenosis location accurately and provide intraoperative clinical assistance. The ResNet-50 algorithm was chosen to facilitate accurate detection of the stenosis site. Results: The high recognition accuracy of the system was confirmed by an average final sensitivity value of 0.96. Since sensitivity is a measure of the probability of a true-positive test, this finding confirms that the system provided accurate guidance to the stenosis area when used for support in actual surgery. Conclusions The proposed method supports surgery for patients with urethral or ureteral strictures by applying the ResNet-50 algorithm. The system analyzes images entering the endoscope during surgery and accurately detects stenosis, thereby assisting in surgery. In future research, we intend to provide both conservative and flexible boundaries of the strictures.

Background/Aims: Idiopathic multicentric Castleman disease (iMCD) comprises approximately 30% of all cases of Castleman disease. It is characterized by constitutional symptoms, enlarged lymph nodes at multiple anatomical sites, and laboratory test abnormalities, which are primarily related to the overproduction of interleukin 6 (IL-6). Siltuximab is a human-mouse chimeric immunoglobulin G1κ monoclonal antibody against human IL-6. In view of the limited treatment options for iMCD, this study aimed to evaluate the efficacy and safety of siltuximab in the management of this condition. Methods: In this real-world retrospective study, we administered siltuximab to 15 patients with iMCD who previously received conventional chemotherapy and/or steroid pulse therapy. The median time to a durable symptomatic response was 22 days (range, 17 to 56). The serum hemoglobin and albumin levels and erythrocyte sedimentation rates significantly normalized after the first 3 months of siltuximab treatment. Lymph node involution, assessed using imaging, was relatively gradual, demonstrating a complete or partial response at 6 months. Results: On an average, the improvements in clinical, laboratory, and radiologic parameters of iMCD in responders were observed after one, three, and eight cycles of siltuximab treatment, respectively. Siltuximab demonstrated a favorable safety profile, and prolonged treatment was well-tolerated. Conclusions Despite the small sample size of the present study, the results are encouraging and demonstrate the potential of siltuximab as the first-line treatment of iMCD. Further large multicenter studies are needed to evaluate the clinical outcomes and adverse events associated with siltuximab.

Purpose: In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. Methods: We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. Results: Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. Conclusions This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.

Purpose: In this study, a urinary management system was established to collect and analyze urinary time and interval data detected through patient-worn smart bands, and the results of the analysis were shown through a web-based visualization to enable monitoring and appropriate feedback for urological patients. Methods: We designed a device that can recognize urination time and spacing based on patient-specific posture and consistent posture changes, and we built a urination patient management system based on this device. The order of body movements during urination was consistent in terms of time characteristics; therefore, sequential data were analyzed and urinary activity was recognized using repeated neural networks and long-term short-term memory systems. The results were implemented as a web (HTML5) service program, enabling visual support for clinical diagnostic assistance. Results: Experiments were conducted to evaluate the performance of the proposed recognition techniques. The effectiveness of smart band monitoring urination was evaluated in 30 men (average age, 28.73 years; range, 26–34 years) without urination problems. The entire experiment lasted a total of 3 days. The final accuracy of the algorithm was calculated based on urological clinical guidelines. This experiment showed a high average accuracy of 95.8%, demonstrating the soundness of the proposed algorithm. Conclusions This urinary activity management system showed high accuracy and was applied in a clinical environment to characterize patients’ urinary patterns. As wearable devices are developed and generalized, algorithms capable of detecting certain sequential body motor patterns that reflect certain physiological behaviors can be a new methodology for studying human physiological behaviors. It is also thought that these systems will have a significant impact on diagnostic assistance for clinicians.

Neurogenic bladder (NB) after spinal cord injury (SCI) is a common complication that inhibits normal daily activities and reduces the quality of life. Regrettably, the current therapeutic methods for NB are inadequate. Therefore, numerous studies have been conducted to develop new treatments for NB associated with SCI. Moreover, a myriad of preclinical and clinical trials on the effects and safety of stem cell therapy in patients with SCI have been performed, and several studies have demonstrated improvements in urodynamic parameters, as well as in sensory and motor function, after stem cell therapy. These results are promising; however, further high-quality clinical studies are necessary to compensate for a lack of randomized trials, the modest number of participants, variation in the types of stem cells used, and inconsistency in routes of administration.

The neurological coordination of the lower urinary tract can be analyzed from the perspective of motor neurons or sensory neurons. First, sensory nerves with receptors in the bladder and urethra transmits stimuli to the cerebral cortex through the periaqueductal gray (PAG) of the midbrain. Upon the recognition of stimuli, the cerebrum carries out decision-making in response. Motor neurons are divided into upper motor neurons (UMNs) and lower motor neurons (LMNs) and UMNs coordinate storage and urination in the brainstem for synergic voiding. In contrast, LMNs, which originate in the spinal cord, cause muscles to contract. These neurons are present in the sacrum, and in particular, a specific neuron group called Onuf’s nucleus is responsible for the contraction of the external urethral sphincter and maintains continence in states of rising vesical pressure through voluntary contraction of the sphincter. Parasympathetic neurons originating from S2–S4 are responsible for the contraction of bladder muscles, while sympathetic neurons are responsible for contraction of the urethral smooth muscle, including the bladder neck, during the guarding reflex. UMNs are controlled in the pons where various motor stimuli to the LMNs are directed along with control to various other pelvic organs, and in the PAG, where complex signals from the brain are received and integrated. Future understanding of the complex mechanisms of micturition requires integrative knowledge from various fields encompassing these distinct disciplines.