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. The purpose of this review article is to review the extent to which VR technology has already influenced certain aspects of medicine, the applications that are currently in use in urology, and the future development trends that could be expected.

PurposeWe 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.MethodsThe literature search was conducted using the MEDLINE, Embase, and Cochrane Library. Keywords used were “BPH,” “tamsulosin,” “placebo.” Experimental groups were defined as tamsulosin 0.2 mg (Tam 0.2) and 0.4 mg (Tam 0.4) and common control group was defined as placebo for indirect treatment comparison. Mixed treatment comparison was performed including one direct comparison study.ResultsSeven studies met the eligible criteria. Indirect treatment comparison revealed that total International Prostate Symptoms Score (IPSS) and quality of life score of IPSS were not significantly different in Tam 0.2 and Tam 0.4 (P>0.05). There was no significant difference of maximal flow rate and postvoid residual urine volume in Tam 0.2 and Tam 0.4 (P>0.05). Mixed treatment comparison including one direct comparison study showed inconsistency (P<0.001). Therefore, analysis using direct treatment comparison effect sizes of Tam 0.2 vs. placebo and Tam 0.4 vs. placebo was done and there was no significant difference.ConclusionsNetwork meta-analysis showed no difference of efficacy between tamsulosin 0.2 mg and 0.4 mg and the evidence of tamsulosin 0.4 mg as initial dose for Asian BPH patient seems to be insufficient. Therefore, initial dose of tamsulosin for Asian BPH patient should be 0.2 mg.

PurposeThis 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.MethodsThis 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.ResultsAn 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.ConclusionsThe 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.

PurposeThough 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.MethodsWe designed a device capable of recognizing urination time and intervals based on specific postures of the patient and consistent changes in posture. These motion data were obtained by a smart band worn on the wrist. An algorithm that recognizes the repetitive and common 3-step behavior for urination (forward movement, urination, backward movement) was devised based on the movement and tilt angle data collected from a 3-axis accelerometer. The sequence of body movements during voiding has consistent temporal characteristics, so we used a recurrent neural network and long short-term memory based framework to analyze the sequential data and to recognize urination time. Real-time data were acquired from the smart band, and for data corresponding to a certain duration, the value of the signals was calculated and then compared with the set analysis model to calculate the time of urination. A comparative study was conducted between real voiding and device-detected voiding to assess the performance of the proposed recognition technology.ResultsThe accuracy of the algorithm was calculated based on clinical guidelines established by urologists. The accuracy of this detecting device was high (up to 94.2%), proving the robustness of the proposed algorithm.ConclusionsThis urination behavior recognition technology showed high accuracy and could be applied in clinical settings to characterize patients’ voiding patterns. As wearable devices are developed and generalized, algorithms detecting consistent sequential body movement patterns reflecting specific physiologic behavior might be a new methodology for studying human physiologic behavior.

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.