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.

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.

Improved Detection of Urolithiasis Using High-Resolution Computed Tomography Images by a Vision Transformer Model.

Purpose: Urinary stones cause lateral abdominal pain and are a prevalent condition among younger age groups. The diagnosis typically involves assessing symptoms, conducting physical examinations, performing urine tests, and utilizing radiological imaging. Artificial intelligence models have demonstrated remarkable capabilities in detecting stones.

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.

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.

Development and Evaluation of an Artificial Intelligence-Based Cognitive Exercise Game: A Pilot Study.

Recently, cognitive serious games have successfully been employed to train cognitive abilities in elderly people with mild cognitive impairment, Alzheimer's disease, and related disorders. However, despite the continuous rehabilitation game design and its applications, the existing cognitive exercise games fall short of user interaction and personalized elements with regard to difficult levels, which leads to users leaving early and losing interests during the gameplay. In this regard, the purpose of the study was to design and develop the serious game inclusive of playful elements for user motivation, the web-based mobile application system for easy accessibility, and Artificial Intelligence- (AI-) based difficulty level adjustment system for prevention from earlier leaving out in the middle of the play so that the elderly users can feel entertaining and immersed into the cognitive game voluntarily.

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.

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.

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.

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.

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.

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

Purpose: Though it is very important obtaining exact data about patients' voiding patterns for managing voiding dysfunction, actual practice is very difficult and cumbersome. In this study, data about urination time and interval measured by smart band device on patients' wrist were collected and analyzed to resolve the clinical arguments about the efficacy of voiding diary. By developing a smart band based algorithm for recognition of complex and serial pattern of motion, this study aimed to explore the feasibility of measurement the urination time and intervals for voiding dysfunction management.

Development of Personalized Urination Recognition Technology Using Smart Bands.

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.

Evidence Is Enough?: A Systematic Review and Network Meta-Analysis of the Efficacy of Tamsulosin 0.2 mg and Tamsulosin 0.4 mg as an Initial Therapeutic Dose in Asian Benign Prostatic Hyperplasia Patients.

Purpose: We compared the efficacy of tamsulosin between 0.2 mg and 0.4 mg in Asian prostatic hyperplasia (BPH) patients using network meta-analysis due to lack of studies with direct comparison.

Erratum: Application of Virtual, Augmented, and Mixed Reality to Urology.

[This corrects the article on p. 172 in vol. 20, PMID: 27706017.

Application of Virtual, Augmented, and Mixed Reality to Urology.

Recent developments in virtual, augmented, and mixed reality have introduced a considerable number of new devices into the consumer market. This momentum is also affecting the medical and health care sector. Although many of the theoretical and practical foundations of virtual reality (VR) were already researched and experienced in the 1980s, the vastly improved features of displays, sensors, interactivity, and computing power currently available in devices offer a new field of applications to the medical sector and also to urology in particular.

Noninvasive identification of viable cell populations in docetaxel-treated breast tumors using ferritin-based magnetic resonance imaging.

Background: Cancer stem cells (CSCs) are highly tumorigenic and are responsible for tumor progression and chemoresistance. Noninvasive imaging methods for the visualization of CSC populations within tumors in vivo will have a considerable impact on the development of new CSC-targeting therapeutics.

Methodology/principal Findings: In this study, human breast cancer stem cells (BCSCs) transduced with dual reporter genes (human ferritin heavy chain [FTH] and enhanced green fluorescence protein [EGFP]) were transplanted into NOD/SCID mice to allow noninvasive tracking of BCSC-derived populations.

Effects of antidepressant treatment on sexual arousal in depressed women: a preliminary FMRI study.

Objective: There was a recent study to explore the cerebral regions associated with sexual arousal in depressed women using functional magnetic resonance imaging (fMRI). The purpose of this neuroimaging study was to investigate the effects of antidepressant treatment on sexual arousal in depressed women.

Methods: SEVEN DEPRESSED WOMEN WITH SEXUAL AROUSAL DYSFUNCTION (MEAN AGE: 41.

Assessment of cerebrocortical areas associated with sexual arousal in depressive women using functional MR imaging.

Introduction: Mental illness is closely related with sexual dysfunction. A number of investigators have reported that depressive women have difficulties in sexual arousal.

Aim: The purpose of this study was to compare the cerebrocortical regions associated with sexual arousal between the healthy and depressive women using functional magnetic resonance imaging (fMRI) based on the blood-oxygenation-level-dependent (BOLD) technique.

Functional MR imaging of psychogenic amnesia: a case report.

We present here a case in which functional MR imaging (fMRI) was done for a patient who developed retrograde psychogenic amnesia for a four year period of her life history after a severe stressful event. We performed the fMRI study for a face recognition task using stimulation with three kinds of face photographs: recognizable familiar faces, unrecognizable friends' faces due to the psychogenic amnesia, and unfamiliar control faces. Different activation patterns between the recognizable faces and unrecognizable faces were found in the limbic area, and especially in the amygdala and hippocampus.