Personalized Urination Activity Recognition Based on a Recurrent Neural Network Using Smart Band

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.

Privacy-Preserving Automatic Collection of Acoustic Voiding Events

Uroflowmetry is a non-invasive diagnostic test used to evaluate the function of the urinary tract. Despite its benefits, it has two main limitations: high intra-subject variability of flow parameters and the requirement for patients to urinate on demand. To overcome these limitations, we have developed a low-cost ultrasonic platform that utilizes machine learning (ML) models to automatically detect and record natural in-home voiding events, without any need for user intervention. This platform operates outside of human-audible frequencies, providing privacy-preserving, automatic uroflowmetries that can be conducted at home as part of daily routines. After evaluating several machine learning algorithms, we found that the Multi-layer Perceptron classifier performed exceptionally well, with a classification accuracy of 97.8% and a low false negative rate of 1.2%. Furthermore, even on lightweight SVM models, performance remains robust. Our results also showed that the voiding flow envelope, helpful for diagnosing underlying pathologies, remains intact even when using only inaudible frequencies.Clinical relevance- This classification task has the potential to be part of an essential toolkit for urology telemedicine. It is especially useful in areas that lack proper medical infrastructure but still host ubiquitous embedded privacy-preserving audio capture devices with Edge AI capabilities.

Current developments in artificial intelligence from obstetrics and gynecology to urogynecology

In today's medical practice clinicians need to struggle with a huge amount of data to improve the outcomes of the patients. Sometimes one clinician needs to deal with thousands of ultrasound images or hundred papers of laboratory results. To overcome this shortage, computers get in help of human beings and they are educated under the term "artificial intelligence." We were using artificial intelligence in our daily lives (i.e., Google, Netflix, etc.), but applications in medicine are relatively new. In obstetrics and gynecology, artificial intelligence models mostly use ultrasound images for diagnostic purposes but nowadays researchers started to use other medical recordings like non-stress tests or urodynamics study results to develop artificial intelligence applications. Urogynecology is a developing subspecialty of obstetrics and gynecology, and articles about artificial intelligence in urogynecology are limited but in this review, we aimed to increase clinicians' knowledge about this new approach.

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.

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.

Artificial intelligence in functional urology: how it may shape the future

PURPOSE OF REVIEW

The aim of the present manuscript is to provide an overview on the current state of artificial intelligence (AI) tools in either decision making, diagnosis, treatment options, or outcome prediction in functional urology.

RECENT FINDINGS

Several recent studies have shed light on the promising potential of AI in functional urology to investigate lower urinary tract dysfunction pathophysiology but also as a diagnostic tool by enhancing the existing evaluations such as dynamic magnetic resonance imaging or urodynamics. AI may also improve surgical education and training because of its automated performance metrics recording. By bringing prediction models, AI may also have strong therapeutic implications in the field of functional urology in the near future. AI may also be implemented in innovative devices such as e-bladder diary and electromechanical artificial urinary sphincter and could facilitate the development of remote medicine.

SUMMARY

Over the past decade, the enthusiasm for AI has been rising exponentially. Machine learning was well known, but the increasing power of processors and the amount of data available has provided the platform for deep learning tools to expand. Although the literature on the applications of AI technology in the field of functional urology is relatively sparse, its possible uses are countless especially in surgical training, imaging, urodynamics, and innovative devices.

Applications of neural networks in urology: a systematic review

PURPOSE OF REVIEW

Over the last decade, major advancements in artificial intelligence technology have emerged and revolutionized the extent to which physicians are able to personalize treatment modalities and care for their patients. Artificial intelligence technology aimed at mimicking/simulating human mental processes, such as deep learning artificial neural networks (ANNs), are composed of a collection of individual units known as 'artificial neurons'. These 'neurons', when arranged and interconnected in complex architectural layers, are capable of analyzing the most complex patterns. The aim of this systematic review is to give a comprehensive summary of the contemporary applications of deep learning ANNs in urological medicine.

RECENT FINDINGS

Fifty-five articles were included in this systematic review and each article was assigned an 'intermediate' score based on its overall quality. Of these 55 articles, nine studies were prospective, but no nonrandomized control trials were identified.

SUMMARY

In urological medicine, the application of novel artificial intelligence technologies, particularly ANNs, have been considered to be a promising step in improving physicians' diagnostic capabilities, especially with regards to predicting the aggressiveness and recurrence of various disorders. For benign urological disorders, for example, the use of highly predictive and reliable algorithms could be helpful for the improving diagnoses of male infertility, urinary tract infections, and pediatric malformations. In addition, articles with anecdotal experiences shed light on the potential of artificial intelligence-assisted surgeries, such as with the aid of virtual reality or augmented reality.

Dysfunctional voiding: update on evaluation and treatment

PURPOSE OF REVIEW

This review will be covering dysfunctional voiding, its diagnosis, and treatment options. This will focus primarily on dysfunctional voiding rather than all lower urinary tract dysfunction and we will focus on some of the newer findings and progress within this disease.

RECENT FINDINGS

Dysfunctional voiding is the inappropriate sphincter and pelvic floor constriction during voiding in an otherwise neurologically normal child. This has a wide spectrum of symptoms and can lead to a number of complications such as chronic kidney disease and poor quality of life if not appropriately addressed. Dysfunctional voiding is diagnosed with a careful examination and history with further imaging including a renal ultrasound and uroflowmetry to confirm the diagnosis. Urotherapy and biofeedback are the first and second-line treatments respectively and lead to significant improvement or cure in the majority of patients. For refractory patients, additional therapy options include use of α-blockers, botulinum injection, and electroneurostimulation, though the majority of the literature surrounding the use of these therapies consists of small studies with heterogenous causes of voiding dysfunction.

SUMMARY

Dysfunctional voiding is a common urologic complaint that has many excellent options for improving the patient's voiding issues and should be considered in patients with voiding dysfunction.

Use of machine learning approach to predict depression in the elderly in China: A longitudinal study

BACKGROUND

Early detection of potential depression among elderly people is conducive for timely preventive intervention and clinical care to improve quality of life. Therefore, depression prediction considering sequential progression patterns in elderly needs to be further explored.

METHODS

We selected 1,538 elderly people from Chinese Longitudinal Healthy Longevity Study (CLHLS) wave 3-7 survey. Long short-term memory (LSTM) and six machine learning (ML) models were used to predict different depression risk factors and the depression risks in the elderly population in the next two years. Receiver operating curve (ROC) and decision curve analysis (DCA) were used to evaluate the prediction accuracy of the reference model and ML models.

RESULTS

The area under the ROC curve (AUC) values of logistic regression with lasso regularisation (AUC=0.629, p-value=0.020) was the highest among ML models. DCA results showed that the net benefit of six ML models was similar (threshold: 0.00-0.10), the net benefit of lasso regression was the largest (threshold: 0.10-0.17 and 0.22-0.25), and the net benefit of DNN was the largest (threshold: 0.17-0.22 and 0.25-0.40). In two ML models, activities of daily living (ADL)/ instrumental ADL (IADL), self-rated health, marital status, arthritis, and number of cohabiting were the most important predictors for elderly with depression.

LIMITATIONS

The retrospective waves used in the LSTM model need to be further increased.

CONCLUSION

The decision support system based on the proposed LSTM+ML model may be very valuable for doctors, nurses and community medical providers for early diagnosis and intervention.

Monitoring and analysis of athletes’ local body movement status based on BP neural network

At present, the body recognition detection of athletes is mostly technical recognition, and the detection of exercise state is less, and the related research is basically blank. Based on this, based on BP neural network algorithm, this study develops athletes’ motion capture based on wearable inertial sensors, and builds a wireless signal transmission scheme based on sensor system. At the same time, this paper constructs the coordinate system to complete the attitude angle settlement and motion recognition and combines the athlete’s actual situation to establish the athlete’s limb trajectory calculation model and analyzes the athletes’ movement patterns. In addition, this paper combines neural network algorithm to analyze, and builds a neural network based athlete body motion recognition model, and analyzes the model effectiveness through simulation system. Studies have shown that when using time domain features+trajectory features as neural network inputs, the hand recognition rate is somewhat improved compared to the use of only time domain features as neural network inputs. It can be seen that the algorithm model of this study has certain validity and can be used as a reference for subsequent related research gradient theory.

Development of a voiding diary using urination recognition technology in mobile environment

We invented a wearable device that can measure voiding time and frequency by checking a habitual series of characteristic motions among men. This study collected and analyzed urination time data collected smart bands worn by patients to resolve the clinical issues posed by using voiding charts. By developing a smart band-based algorithm for assessing urination time in patients, this study aimed to explore the feasibility of urination management systems. This study aimed to assess urination time based on a patient’s posture and changes in posture. Motion data were obtained from a smart band on the arm. An algorithm that identifies the three stages of urination (forward movement, urination, backward movement) was developed based on data collected from a 3-axis accelerometer and tilt angle data. Therefore, we analyze hidden Markov model (HMM)-based sequential data to determine urination time. Real-time data were acquired from the smart band. For data corresponding to a specific duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. The final accuracy of the algorithm was calculated based on clinical guidelines for urologists. The experiment showed a high average accuracy of 92.5%, proving the robustness of the proposed algorithm. The proposed urination time recognition technology draws on acceleration data and tilt angle data collected via a smart band; these data were then analyzed using a classifier after applying the HMM method.

Development of intelligent healthcare system based on ambulatory blood pressure measuring device

Currently, the market size of blood pressure monitors both in domestic and overseas is gradually increasing due to the increase in hypertension patients resulting from aging population. In addition, the necessity of developing systems and devices for the healthcare of hypertension patients is also increasing. Moreover, the determination of health normality in respect to the management of hypertension patients is possible, but it is essentially important to incorporate preventive healthcare. Thus, further studies on deep learning-based prediction technology using previous data are needed. This paper proposes the development of an intelligent healthcare management system that can help to manage the health of hypertensive patients. The system includes a wrist-worn ambulatory blood pressure monitoring device that can analyze the normality of measured blood pressures. The performance evaluation results of the proposed system verified the reliability of data acquisition as compared with the existing equipment as well as the efficiency of the intelligent healthcare system.

The Prospect of a New Smart Healthcare System: A Wearable Device-Based Complex Structure of Position Detecting and Location Recognition System

In upcoming fourth industrial revolution era, it is inevitable to address smart healthcare as not only scientist but also clinician. We have the task to plan and realize this through human imagination, creativity, and applicability for the clarification of the direction of the development and utilization of this technology. One thing that is clear is that it is important to understand what information is needed, how to interpret it, what will be the outcomes, and how to respond in artificial intelligence and Internet of Things era. Therefore, we would like to briefly discuss the characteristics of smart healthcare, and, suggest one approach that is easily applicable in the current situation.