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: Waist circumferences are widely used to diagnosis and assessment of obesity but various standards of the measuring waist circumference originate the confusion on diagnosis and assessment of obesity. According to this, this study is willing to light up standards of measuring waist circumference what is intimate with body mass index(BMI) and have a precision of intra observer and inter observers in repeated measurements of the waist. METHODS: Measuring of waist circumference by the 4 different standards(anterior superior iliac crest(ASIC), umbilicus, above 3 cm to ASIC, the thinnest area in the waist by the range of seeing) were performed on 102 people visiting Health Promotion Center of one medical collage hospital by two observers in each 2 times, total 16 times. Each data were analyzed by the relativity between the BMI and the each means of measuring waist circumference and by ANOVA test in intra observer and inter observer bias. RESULTS: The relation between BMI and data in measuring standard of ASIC of the waist circumference is the highest. (r=0.900) and the following umbilicus(r=0.896), above 3 cm to the anterior superior iliac crest(r=0.888), the thinnest area in the waist by the range of seeing(r=0.877), It is the lowest in intra observer bias that the thinnest area in the waist by the range of seeing, and ASIC in inter observer. CONCLUSION: It is desirable that ASIC is the standard of measuring waist circumference in the diagnosis of obesity and in measuring that by different two person, because of the highest relativity in BMI and the lowest inter observers bias. But the thinnest area in the waist by the range of seeing is desirable in measuring by same person because of intra observer bias.
Anthropometry ; Bias (Epidemiology) ; Body Constitution ; Body Mass Index ; Diagnosis ; Health Promotion ; Humans ; Obesity ; Observer Variation ; Umbilicus ; Waist Circumference*

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.

PURPOSE: This study collected and analyzed activity data sensed through smart bands worn by patients in order to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for recognizing urination activity in patients, this study aimed to explore the feasibility of urination monitoring systems. METHODS: This study aimed to develop an algorithm that recognizes urination based on a patient's posture and changes in posture. Motion data was obtained from a smart band on the arm. An algorithm that recognizes the 3 stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and from tilt angle data. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the absolute value of the signals was calculated and then compared with the set threshold value to determine the occurrence of vibration signals. In feature extraction, the most essential information describing each pattern was identified after analyzing the characteristics of the data. The results of the feature extraction process were sorted using a classifier to detect urination. RESULTS: An experiment was carried out to assess the performance of the recognition technology proposed in this study. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 90.4%, proving the robustness of the proposed algorithm. CONCLUSIONS: The proposed urination recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after comparative analyses with standardized feature patterns.
Acceleration ; Arm ; Humans ; Posture ; Urination* ; Vibration

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.

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.

Objective: The purpose of this study was to evaluate and compare the association of job stress and working with video display terminal (VDT) to musculoskeletal symptoms of the neck-shoulder which were most common in white-collar workers. METHODS: From 122 workplaces, 1,790 white-collar workers with no trauma, and no history of musculoskeletal disease were selected for the study. The questionnaire survey included general characteristics, work related characteristics, Job Content Questionnaire (JCQ) and musculoskeletal symptoms. Multiple logistic regression, adjusted for age, smoking status, drinking habit, housekeeping, work time, job tenure, and work-load change, were used to evaluate the effects of job stress and VDT-work on the symptoms. RESULTS: The prevalence of neck-shoulder symptoms was overall 24.3% overall. The prevalence odds ratio of job demand (high/low) to neck-shoulder symptoms, adjusted for general and work-related factors, was 1.56 (95% confidence interval 1.12~2.17), and that of job strain (high strain/low strain) was 1.72 (1.07~2.79). However, VDT-work was not associated with neck-shoulder symptoms in the multiple logistic regression model. CONCLUSIONS: To prevent musculoskeletal disorders in white-collar workers, it is important to consider psychosocial factors such as job demand and job strain, as well as VDT-work.
Computer Terminals ; Drinking ; Housekeeping ; Logistic Models ; Musculoskeletal Diseases ; Neck* ; Odds Ratio ; Prevalence ; Psychology ; Questionnaires ; Shoulder* ; Smoke ; Smoking

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.

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: 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.