Classification of Bladder Emptying Patterns by LSTM Neural Network Trained Using Acoustic Signatures.

(1) Background: Non-invasive uroflowmetry is used in clinical practice for diagnosing lower urinary tract symptoms (LUTS) and the health status of a patient. To establish a smart system for measuring the flowrate during urination without any temporospatial constraints for patients with a urinary disorder, the acoustic signatures from the uroflow of patients being treated for LUTS at a tertiary hospital were utilized. (2) Methods: Uroflowmetry data were collected for construction and verification of a long short-term memory (LSTM) deep-learning algorithm. The initial sample size comprised 34 patients; 27 patients were included in the final analysis. Uroflow sounds generated from flow impacts on a structure were analyzed by loudness and roughness parameters. (3) Results: A similar signal pattern to the clinical urological measurements was observed and applied for health diagnosis. (4) Conclusions: Consistent flowrate values were obtained by applying the uroflow sound samples from the randomly selected patients to the constructed model for validation. The flowrate predicted using the acoustic signature accurately demonstrated actual physical characteristics. This could be used for developing a new smart flowmetry device applicable in everyday life with minimal constraints from settings and enable remote diagnosis of urinary system diseases by objective continuous measurements of bladder emptying function.

Wearable E-Textile and CNT Sensor Wireless Measurement System for Real-Time Penile Erection Monitoring.

Erection measurements are the most important indicator of male urological disease diagnosis, treatment, and results. Rigiscan has been used widely in studies and diagnoses for nocturnal penile tumescence for evaluating erectile dysfunction by measuring the number and timing of erectile dysfunctions during sleep. However, this device has limitations such as the weight and bulk of the device and has been questioned for its role as a standard for ED Erectile Dysfunction (ED) diagnosis. In this study, we propose a real-time wearable monitoring system that can quantitatively measure the length and circumference of the penis using electronic textiles (E-textile) and carbon nanotube (CNT) sensors. The E-textile sensor is used to measure the length, circumference, and gradient with portability, convenience, and comfort. Sensors were created by coating CNTs on latex for flexibility. The CNT-based latex condom-type sensor in our proposed system shows the length, circumference, and curvature measurements with changes in resistance, and the E-textile performance shows a 1.44% error rate and a cavity radius of 110 to 300. The results of this conceptual study are for supplementary sensor development with a combination of new technologies with alternatives or existing methods for measuring erection function.

Positive surgical margin in robot-assisted radical prostatectomy: correlation with pathology findings and risk of biochemical recurrence.

BACKGROUND: To analyze the correlation of surgical margin status with other findings on final pathology and risk of biochemical recurrence (BCR) in patients undergoing robot-assisted radical prostatectomy (RALP). METHODS: Bundang Prostatectomy Database was reviewed to identify patients who underwent RARP from 2007 to 2011 and had a positive surgical margin (PSM) on final pathology. Pathology findings were reviewed. BCR-free survival was calculated using the Kaplan-Meier method. Cox univariable and multi-variable regression models were used to find the correlation between clinicopathologic factors and BCR. RESULTS: Eight hundred and fifteen patients were included in the analysis: 118 (14.48%) had apical positive margin, 152 (18.65%) had a positive margin in another site, and 545 (66.87%) had negative surgical margins. In patients with only apical PSM, stratified by clinical stage, Kaplan-Meier analysis demonstrated significant difference in BCR-free survival between the groups (log rank P<0.001). Multivariable Cox proportional hazards model showed maximal percentage of positive core is the strongest predictor of BCR (HR=3.131, P<0.001). Multivariable Cox proportional hazards model showed PSM is one of the powerful predictor of postoperative BCR (HR=3.123, P<0.001). CONCLUSIONS: PSM after RALP is one of the powerful predictor of BCR and apical PSM is relatively less powerful predictor of BCR. Maximal percentage of positive core is the most powerful preoperative predictors of BCR. Clinical stage and biopsy Gleason score are also associated with pathologic outcomes and BCR free survival rates in patients with positive apical margin only.