Past, Present, and Future in the Study of Neural Control of the Lower Urinary Tract

Research and progress on the mechanism of lower urinary tract neuromodulation: a literature review

The storage and periodic voiding of urine in the lower urinary tract are regulated by a complex neural control system that includes the brain, spinal cord, and peripheral autonomic ganglia. Investigating the neuromodulation mechanisms of the lower urinary tract helps to deepen our understanding of urine storage and voiding processes, reveal the mechanisms underlying lower urinary tract dysfunction, and provide new strategies and insights for the treatment and management of related diseases. However, the current understanding of the neuromodulation mechanisms of the lower urinary tract is still limited, and further research methods are needed to elucidate its mechanisms and potential pathological mechanisms. This article provides an overview of the research progress in the functional study of the lower urinary tract system, as well as the key neural regulatory mechanisms during the micturition process. In addition, the commonly used research methods for studying the regulatory mechanisms of the lower urinary tract and the methods for evaluating lower urinary tract function in rodents are discussed. Finally, the latest advances and prospects of artificial intelligence in the research of neuromodulation mechanisms of the lower urinary tract are discussed. This includes the potential roles of machine learning in the diagnosis of lower urinary tract diseases and intelligent-assisted surgical systems, as well as the application of data mining and pattern recognition techniques in advancing lower urinary tract research. Our aim is to provide researchers with novel strategies and insights for the treatment and management of lower urinary tract dysfunction by conducting in-depth research and gaining a comprehensive understanding of the latest advancements in the neural regulation mechanisms of the lower urinary tract.

Artificial Intelligence-Based Patient Monitoring System for Medical Support

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%.

CONCLUSION

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.

The Current State of Artificial Intelligence Application in Urology

Artificial intelligence (AI) is being used in many areas of healthcare, including disease diagnosis and personalized treatment and rehabilitation management. Medical AI research and development has primarily focused on diagnosis, prediction, treatment, and management as an aid to patient care. AI is being utilized primarily in the areas of personal healthcare and diagnostic imaging. In the field of urology, significant investments are being made in the development of urination monitoring systems in the field of personal healthcare and ureteral stricture and urinary stone diagnosis solutions in the field of diagnostic imaging. In addition, AI technology is also being applied in the field of neurogenic bladder to develop risk monitoring systems based on video and audio data. This paper examines the application of AI to urological diseases and discusses the current trends and future prospects of AI research.

Development and Evaluation of Urolithiasis Detection Technology Based on a Multimethod Algorithm

Purpose: In this paper, we propose an optimal ureter stone detection model utilizing multiple artificial intelligence technologies. Specifically, the proposed model of urinary tract stone detection merges an artificial intelligence model and an image processing model, resulting in a multimethod approach.Methods: We propose an optimal urinary tract stone detection algorithm based on artificial intelligence technology. This method was intended to increase the accuracy of urinary tract stone detection by combining deep learning technology (Fast R-CNN) and image processing technology (Watershed).Results: As a result of deriving the confusion matrix, the sensitivity and specificity of urinary tract stone detection were calculated to be 0.90 and 0.91, and the accuracy for their position was 0.84. This value was higher than 0.8, which is the standard for accuracy. This finding confirmed that accurate guidance to the stones area was possible when the developed platform was used to support actual surgery.Conclusions: The performance evaluation of the method proposed herein indicated that it can effectively play an auxiliary role in diagnostic decision-making with a clinically acceptable range of safety. In particular, in the case of ambush stones or urinary stones accompanying ureter polyps, the value that could be obtained through combination therapy based on diagnostic assistance could be evaluated.

New Trends in Innovative Technologies Applying Artificial Intelligence to Urinary Diseases

Artificial intelligence (AI) is used in various fields of medicine, with applications encompassing all areas of medical services, such as the development of medical robots, the diagnosis and personalized treatment of diseases, and personalized healthcare. Medical AI research and development have been largely focused on diagnosis, prediction, treatment, and management as an auxiliary means of patient care. AI is mainly used in the fields of personal healthcare and diagnostic imaging. In urology, substantial investments are being made in the development of urination monitoring systems in the personal healthcare field and diagnostic solutions for ureteral stricture and urolithiasis in the diagnostic imaging field. This paper describes AI applications for urinary diseases and discusses current trends and future perspectives in AI research.

Novel botulinum neurotoxin-A tibial nerve perineural injection to alleviate overactive bladder symptoms in male rats

Although tibial nerve modulation has shown to induce positive changes in the overactive bladder (OAB), prolonged therapeutic effects using percutaneous stimulation have not yet been achieved. Intradetrusor onabotulinum toxin A injection can provide prolonged therapeutic effects; however, its delivery requires invasive measures. By applying local relief of tibial nerve neural entrapment with onabotulinum toxin A injection, this study investigated the feasibility and efficacy of combining the abovementioned two therapeutic strategies. An OAB animal model was developed using 12 adult Sprague-Dawley rats with cyclophosphamide intraperitoneal injection. A perineural injection site comparable to the tibial nerve perineural injection site and corresponding to that in humans was identified and developed in rats. The toxin was injected five days after establishing the OAB. The incision was made in the skin on the lateral surface of the thigh. The biceps femoris muscle was cut across, exposing the sciatic nerve and its three terminal branches: the sural, common peroneal, and tibial nerves, and 100 units of onabotulinum toxin A was injected into the surrounding tissue. Five days following injection, cystometry was performed. Inter-contraction time, contraction pressure, and interval of the disease state improved with statistical significance. The OAB animal model showed significant improvement with the tibial nerve perineural injection of botulinum toxin, thereby suggesting the possibility of a comparable treatment adaptation in humans.

A Study on the Optimal Artificial Intelligence Model for Determination of Urolithiasis

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.

White Matter Integrity in Men With Benign Prostatic Hyperplasia and Bladder Outlet Obstruction and Its Contribution to Lower Urinary Tract Symptoms

Purpose

Lower urinary tract symptoms (LUTS) associated with bladder outlet obstruction (BOO) due to benign prostatic hyperplasia (BPH) can negatively impact quality of life. We evaluated the structural connectivity of the brain in men with BPH with chronic BOO using diffusion tensor imaging (DTI).

Methods

Ambulatory male patients aged ≥45 years with BPH and BOO were recruited. LUTS was defined as an International Prostate Symptom Score (IPSS) ≥12 and a maximum urinary flow rate ≤15 mL/sec. Upon recruitment, uroflowmetry and validated questionnaires regarding bladder status were collected. DTI images from each subject were aligned with the ICBM-DTI-81 atlas, defining 50 white matter tracts (WMTs). The mean values of DTI parameters—fractional anisotropy and mean diffusivity—for each WMT were extracted. These measures were then utilized to compute Pearson correlation coefficients with clinical parameters. Objective clinical parameters included uroflowmetry parameters, postvoid residual (PVR) volume, and bladder capacity. Subjective clinical parameters were assessed using validated questionnaires: the IPSS, Incontinence Symptom Index, and Sexual Health Inventory for Men.

Results

The correlation analysis revealed 15 WMTs that showed statistically significant associations (P<0.05) with objective and subjective clinical parameters. Eight tracts were associated with uroflowmetry parameters: maximum flow rate (Qmax), mean flow rate (Qmean), and PVR. Among these tracts, the middle cerebellar peduncles and left medial lemniscus were associated with Qmax; the genu of the corpus callosum, left superior corona radiata, corticospinal tract, right medial lemniscus, posterior corona radiata with Qmean; and the left posterior corona radiata with PVR. Seven tracts also demonstrated significant associations with the IPSS.

Conclusions

Our results suggest correlations between the preserved white matter integrity of specific WMTs and the severity of LUTS based on objective and subjective clinical parameters, leading us to believe that a distinct pathology of the central nervous system might exist.

Numerical investigation of urethra flow characteristics in benign prostatic hyperplasia

BACKGROUND AND OBJECTIVE

Conventional practice includes a limited depiction of urethral pressure and flows based on fragmented gross clinical observations. However, with technological advancements in simulations, computational fluid dynamics (CFD) can provide an alternative approach to predict the bladder pressure with a concordant quantitative flow field in the urethra. Thus, this study aims to comprehensively analyze the urine flow characteristics in various urethra models using simulations.

METHODS

Three-dimensional urethra models were constructed for seven specific subjects based on clinical radiographs. Simulations with Reynolds averaged Navier-Stokes model were performed to quantitatively investigate the urine flow under various volume flow rate of voided urine.

RESULTS

Under benign prostatic hyperplasia, the spindle shape of the prostatic urethra (PRU) generates wake flow. The wake flow was also observed in several regions downstream of the PRU, depending on the urethra shape. This wake flow resulted in total pressure loss and urinary tract dysfunction. When comparing pre- and post-operative urethra models, the bladder pressure decreased by 14.98% in P04 and 4.67% in P06. Thus, we identified variability between surgical results of patients. The bladder pressure according to the volume flow rate of voided urine was investigated using simulations and the theoretical consideration based on hydrodynamics. In theoretical consideration, the bladder pressure was expressed as a second-order polynomial for volume flow rate. These results concur with the simulation results.

CONCLUSION

Numerical simulation can describe the urine flow field in the urethra, providing the possibility to predict the bladder pressure without requiring painful, invasive interventions, such as cystoscopy. Furthermore, effective treatments to improve urination function can be formulated to be patient-specific, by detecting causes and problem regions based on quantitative analysis and predicting post-surgical outcomes.

Development of an Artificial Intelligence-Based Support Technology for Urethral and Ureteral Stricture Surgery

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.

Development of Early-Stage Stroke Diagnosis System for the Elderly Neurogenic Bladder Prevention

Purpose

There are various neurogenic bladder patterns that occur in patients during stroke. Among these patterns, the focus was mainly on the patient’s facial parsy diagnosis. Stroke requires early response, and it is most important to identify initial symptoms such as facial parsy. There is an urgent need for a diagnostic technology that notifies patients and caregivers of the onset of disease in the early stages of stroke. We developed an artificial intelligence (AI) stroke early-stage analysis software that can alert the early stage of stroke through analysis of facial muscle abnormalities for the elderly neurogenic bladder prevention.

Methods

The method proposed in this paper developed a learning-based deep learning analysis technology that outputs the initial stage of stroke after acquiring a high-definition digital image and then deep learning face analysis. The applied AI model was applied as a multimodal deep learning concept. The system is linked and integrated with the existing urine management integrated system to support patient management with a total-care concept.

Results

We developed an AI stroke early-stage analysis software that can alert the early stage of stroke with 86% hit performance through analysis of facial muscle abnormalities in the elderly. This result shows the validation result of the landmark image learning model based on the distance learning model.

Conclusions

We developed an AI stroke early-stage diagnostic system as a wellness personal medical service plan and prevent cases of missing golden time when existing stroke occurs. In order to secure and facilitate distribution of this, it was developed in the form of AI analysis software so that it can be mounted on various hardware products. In the end, it was found that using AI for these stroke diagnoses and making them quickly and accurately had a positive effect indirectly, if not directly, on the neurogenic bladder.

Pelvic floor muscle exercise and training for coping with urinary incontinence

The pelvic floor consists of levator ani muscles including puborectalis, pubococcygeus and iliococcygeus muscles, and coccygeus muscles. Pelvic floor muscle exercise (PFME) is defined as exercise to improve pelvic floor muscle strength, power, endurance, relaxation, or a combination of these parameters. PFME strengthens the pelvic floor muscles to provide urethral support to prevent urine leakage and suppress urgency. This exercise has been recommended for urinary incontinence since first described by Kegel. When treating urinary incontinence, particularly stress urinary incontinence, PFME has been recommended as first-line treatment. This article provides clinical application of PFME as a behavioral therapy for urinary incontinence. Clinicians and physical therapist should understand pelvic floor muscle anatomy, evaluation, regimen, and instruct patients how to train the muscles properly.

A Review of Aging and the Lower Urinary Tract: The Future of Urology

Lower urinary tract symptoms (LUTS) are common among elderly people, with significant effects on individuals, caregivers, and the wider health care system. As the elderly population with multiple comorbidities is increasing, the burden of LUTS will increase. This review describes the demographic trends in the aging society, changes in lower urinary tract function with aging, and deterioration of physical and cognitive function in aging, as well as what has been done regarding geriatric urology and what urologists should do to meet the health care needs of the aging population. Frailty and dementia are unmissable factors in the evaluation of elderly patients. Numerous reports have described associations between LUTS and frailty and between LUTS and dementia. Urologists must be aware of the multiplex physical, cognitive, and social characteristics of elderly people. Maintaining a geriatric viewpoint in the diagnosis, treatment, and management of elderly individuals with LUTS will fulfill the unmet needs of elderly people. It is also essential to discuss the treatment and management goals of LUTS with patients and caregivers. Active case identification, appropriate evaluations of LUTS and comorbidities, and a multidisciplinary approach with other health-care professionals are recommended for better treatment and management.

Sophisticated regulation of micturition: review of basic neurourology

The neurological regulation of the lower urinary tract can be viewed separately from the perspective of sensory neurons and motor neurons. First, in the receptors of the bladder and urethra of sensory nerves, sensations are transmitted through the periaqueductal gray matter of the midbrain to the cerebral cortex, and the cerebrum goes through the process of decision-making. Motor neurons are divided into upper motor neurons (UMNs) and lower motor neurons (LMNs). UMNs coordinate storage and micturition in the brain stem so that synergic voiding can occur. LMNs facilitate muscle contractions in the spinal cord. The muscles involved in urinary storage and micturition are innervated by the somatic branches of sympathetic, parasympathetic, and peripheral nerves. Sympathetic nerves are responsible for contractions of urethral smooth muscles, while parasympathetic nerves originate from S2–S4 and are in charge of contractions of the bladder muscle. Somatic nerves originate from the motor neurons in Onuf’s nucleus, which is a specific part of somatic nerves. In this review, we will investigate the structures of the nervous systems related to the lower urinary tract and the regulatory system of innervation for the urinary storage and micturition and discuss the clinical significance and future prospects of neurourological research.

Applications of artificial intelligence in urological setting: a hopeful path to improved care

Artificial intelligence (AI) has been introduced in urology research and practice. Application of AI leads to better accuracy of disease diagnosis and predictive model for monitoring of responses to medical treatments. This mini-review article aims to summarize current applications and development of AI in urology setting, in particular for diagnosis and treatment of urological diseases. This review will introduce that machine learning algorithm-based models will enhance the prediction accuracy for various bladder diseases including interstitial cystitis, bladder cancer, and reproductive urology.

Personalized Urination Activity Management Based on an Intelligent System Using a Wearable Device

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.

Bacterial Colonization as a Possible Source of Overactive Bladder Symptoms in Pediatric Patients: A Literature Review

Overactive Bladder (OAB) is a common condition that is known to have a significant impact on daily activities and quality of life. The pathophysiology of OAB is not completely understood. One of the new hypothetical causative factors of OAB is dysbiosis of an individual urinary microbiome. The major aim of the present review was to identify data supporting the role of bacterial colonization in overactive bladder symptoms in children and adolescents. The second aim of our study was to identify the major gaps in current knowledge and possible areas for future clinical research. There is a growing body of evidence indicating some relationship between qualitative and quantitative characteristics of individual urinary microbiome and OAB symptoms in adult patients. There are no papers directly addressing this issue in children or adolescents. After a detailed analysis of papers relating urinary microbiome to OAB, the authors propose a set of future preclinical and clinical studies which could help to validate the concept in the pediatric population.