Yuan-Hung Pong

Background/Objectives: For aging men experiencing lower urinary tract symptoms (LUTS), bladder diaries (BD) and uroflowmetry (UFM) are commonly used non-invasive diagnostic tools. However, bladder diaries often suffer from subjectivity and incomplete data, while traditional hospital-based uroflowmetry lacks convenience and repeatability. Therefore, there is a growing need for a user-friendly, artificial intelligence (AI)-powered at-home uroflow monitoring solution. This study aims to develop a novel, vibration-based home uroflowmetry system capable of recognizing uroflow curve patterns and measuring voiding parameters, and to compare its performance with other existing home-based uroflowmetry methods. Methods: Seventy-six male participants, all of whom provided informed consent, underwent uroflowmetry to assess voiding symptoms. An accelerometer affixed to the uroflowmeter’s urine container captured vibration signals, which were used to calculate the root mean square (RMS) values and maximum amplitude (Mmax). Simultaneously, the uroflowmeter recorded standard voiding parameters and generated uroflow curves. These vibration signals were then analyzed using a convolutional neural network (CNN) to classify six distinct uroflow curve patterns, aiding in diagnostic evaluation. Results: Seventy-six participants’ voiding volume ranged from 50 mL to 690 mL (median [Q1, Q3]: 160 [70.00, 212.50] mL). The correlation analysis revealed positive correlations between the vibration signals and voiding parameters, including the voided volume and RMS (R = 0.768, p < 0.001), Qmax and Mmax (R = 0.684, p < 0.001), voiding time and signal time (R = 0.838, p < 0.001), time to Qmax and time to Mmax (R = 0.477, p < 0.001). AI pattern recognition demonstrated high accuracy with all three indicators (precision, recall, and F1 score) surpassing 0.97. Conclusions: This AI-assisted vibration-based home uroflowmetry enables accurate voiding parameter measurement and uroflow pattern recognition, showing high precision, recall, and F1-score. It might offer a convenient solution for continuous and subjective bladder monitoring outside clinical settings.

Although uroflowmetry and bladder diaries are widely used for noninvasive evaluation of lower urinary tract symptoms, they still have limitations in diagnostic capability and users’ convenience. The aim of this paper is to discuss potential solutions by reviewing (1) the evolution and current clinical use of uroflowmetry and bladder diary, including clinical guidelines, daily practice applications, and their historical development; (2) a growing trend toward using home devices with various technologies; and (3) a comprehensive comparison of the strengths and weaknesses of these home devices. In our opinion, the following points can be highlighted: (1) the emerging trend of using home devices can enhance diagnostic capabilities through repeated measurements and the convenience of at-home testing and (2) home devices, which provide both frequency-volume and uroflowmetry information, have the potential to transform the management of lower urinary tract symptoms.

BACKGROUND With an aging population, patients complaining about low urinary tract symptoms (LUTS) increased year by year. For functional evaluation of low urinary tract (LUT), bladder diary (BD) and uroflowmetry (UFM) are both common non-invasive examinations for patients with LUTS. Voiding pattern of a patient is currently measured by hospital uroflowmetry and/or home bladder diaries. However, bladder diaries are less objective and often contain missed data, while hospital uroflowmetry lacks repeated measurements and convenience. Thus, it is important to develop a convenient and artificial intelligence(AI) based at-home uroflow monitoring. OBJECTIVE This study developed an innovative home vibration-based uroflowmetry for uroflow-curve pattern recognition and voiding parameters measurement and compared it with other home uroflowmetry. METHODS Seventy-six male participants with informed consents received uroflowmetry for voiding symptoms. An accelerometer attached on the urine bucket of the uroflowmeter detects vibration signals generating root mean square (RMS), maximal amplitude (Mmax) and the uroflowmeter measures voiding parameters and creates uroflow curves simultaneously. Vibration signals were processed with an artificial intelligence (AI) model (Convolution Neural Network) to recognize six patterns of uroflow curves to assist diagnosis. RESULTS Seventy-six participants’ voiding volume ranged from 50 ml to 690 ml (average: 192.50 ±155.58 ml). The correlation analysis revealed positive correlations between voided volume and RMS (R=0.768, p<.001), maximal flow rate (Qmax) and Mmax (R=0.684, p<.001), voiding time and signal time (R=0.838, p<.001), time to Qmax and time to Mmax (R=0.477, p<.001). AI pattern recognition demonstrated high accuracy with all three indicators (precision, recall, and F1 score) surpassing 0.97. CONCLUSIONS Usage of home uroflowmetry increases because of convenience, repeated measurement, and big data application. This vibration-based home uroflowmetry with AI assistance is feasible for at-home voiding parameters measurement and uroflow-curve pattern recognition. Compared with other technologies, vibration-based uroflowmetry demonstrates advantages for intuitive and contact-free usage of home uroflowmetry. CLINICALTRIAL The research protocol was approved by the institutional review board (IRB: 107-B-10-01).

UNSTRUCTURED Although uroflowmetry and bladder diaries are widely used for noninvasive evaluation of lower urinary tract symptoms, they still have limitations in diagnostic capability and users’ convenience. The aim of this paper is to discuss potential solutions by reviewing (1) the evolution and current clinical use of uroflowmetry and bladder diary, including clinical guidelines, daily practice applications, and their historical development; (2) a growing trend toward using home devices with various technologies; and (3) a comprehensive comparison of the strengths and weaknesses of these home devices. In our opinion, the following points can be highlighted: (1) the emerging trend of using home devices can enhance diagnostic capabilities through repeated measurements and the convenience of at-home testing and (2) home devices, which provide both frequency-volume and uroflowmetry information, have the potential to transform the management of lower urinary tract symptoms.

In a clinical context, there are increasing numbers of people with voiding dysfunction. To date, the methods of monitoring the voiding status of patients have included voiding diary records at home or urodynamic examinations at hospitals. The former is less objective and often contains missing data, while the latter lacks frequent measurements and is an invasive procedure. In light of these shortcomings, this study developed an innovative and contact-free technique that assists in clinical voiding dysfunction monitoring and diagnosis. Vibration signals during urination were first detected using an accelerometer and then converted into the mel-frequency cepstrum coefficient (MFCC). Lastly, an artificial intelligence model combined with uniform manifold approximation and projection (UMAP) dimensionality reduction was used to analyze and predict six common patterns of uroflowmetry to assist in diagnosing voiding dysfunction. The model was applied to the voiding database, which included data from 76 males aged 30 to 80 who required uroflowmetry for voiding symptoms. The resulting system accuracy (precision, recall, and f1-score) was around 98% for both the weighted average and macro average. This low-cost system is suitable for at-home urinary monitoring and facilitates the long-term uroflow monitoring of patients outside hospital checkups. From a disease treatment and monitoring perspective, this article also reviews other studies and applications of artificial intelligence-based methods for voiding dysfunction monitoring, thus providing helpful diagnostic information for physicians.

(1) Background: To further validate METCAM/MUC18 as a diagnostic biomarker for prostate cancer, a modified Lateral Flow Immune Assay (LFIA) with increased sensitivity and specificity was designed by taking advantage of the extremely high affinity between biotin and streptavidin and used. (2) Methods: The combination of a commercial biotinylated rabbit antibody (EPP11278), or the home-made biotinylated chicken antibody, and the nano-gold conjugated home-made chicken antibody or a commercial rabbit antibody (EPP11278), had the higher sensitivity and specificity in this modified LFIA to establish calibration curves from the two recombinant METCAM/MUC18 proteins and were used for determining METCAM/MUC18 concentrations in serum specimens from normal individuals, benign prostatic hyperplasia (BPH) patients, prostatic intraepithelial neoplasia (PIN) patients, prostate cancer patients with various Gleason scores, and treated patients. (3) Results: Data obtained by this modified LFIA were statistically better than traditional LFIA and prostate-specific antigen (PSA) test. Interestingly, serum METCAM/MUC18 concentrations were higher in pre-malignant PIN patients than prostate cancer patients and both were higher than normal individuals, BPH patients, and treated patients. Serum METCAM/MUC18 concentrations were directly proportional to most serum PSA. (4) Conclusions: Elevated serum METCAM/MUC18 concentrations may be used for predicting the malignant potential of prostate cancer at an early premalignant (PIN) stage, which is not achievable by the current PSA test.

Background: Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy to monitor sperm motility. Objective: This study verified the performance of an artificial intelligence–based image recognition and cloud computing sperm motility testing system (Bemaner, Createcare) composed of microscope and microfluidic modules and designed to adapt to different types of smartphones. Methods: Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed by an artificial intelligence–based image recognition algorithm then results were displayed. According to the number of motile sperm in the vision field, 47 (deidentified) videos of sperm were scored using 6 grades (0-5) by a male-fertility expert with 10 years of experience. Pearson product-moment correlation was calculated between the grades and the results (concentration of total sperm, concentration of motile sperm, and motility percentage) computed by the system. Results: Good correlation was demonstrated between the grades and results computed by the system for concentration of total sperm (r=0.65, P<.001), concentration of motile sperm (r=0.84, P<.001), and motility percentage (r=0.90, P<.001). Conclusions: This smartphone-based sperm motility test (Bemaner) accurately measures motility-related parameters and could potentially be applied toward the following fields: male infertility detection, sperm quality test during preparation for pregnancy, and infertility treatment monitoring. With frequent at-home testing, more data can be collected to help make clinical decisions and to conduct epidemiological research.

BACKGROUND Human sperm quality fluctuates over time. Therefore, it is crucial for couples preparing for natural pregnancy to monitor sperm motility. OBJECTIVE This study verified the performance of an artificial intelligence–based image recognition and cloud computing sperm motility testing system (Bemaner, Createcare) composed of microscope and microfluidic modules and designed to adapt to different types of smartphones. METHODS Sperm videos were captured and uploaded to the cloud with an app. Analysis of sperm motility was performed by an artificial intelligence–based image recognition algorithm then results were displayed. According to the number of motile sperm in the vision field, 47 (deidentified) videos of sperm were scored using 6 grades (0-5) by a male-fertility expert with 10 years of experience. Pearson product-moment correlation was calculated between the grades and the results (concentration of total sperm, concentration of motile sperm, and motility percentage) computed by the system. RESULTS Good correlation was demonstrated between the grades and results computed by the system for concentration of total sperm (r=0.65, P<.001), concentration of motile sperm (r=0.84, P<.001), and motility percentage (r=0.90, P<.001). CONCLUSIONS This smartphone-based sperm motility test (Bemaner) accurately measures motility-related parameters and could potentially be applied toward the following fields: male infertility detection, sperm quality test during preparation for pregnancy, and infertility treatment monitoring. With frequent at-home testing, more data can be collected to help make clinical decisions and to conduct epidemiological research.

Emphysematous pyelonephritis (EPN) is a severe necrotizing infection of the renal parenchyma and perirenal tissues that is caused by gas-producing bacterial pathogens. Percutaneous drainage is now the gold standard of definitive management. The aim of this study is to analyze the predictors associated with failure of conservative treatment among patients with EPN and offer the recommendation of appropriate empirical antibiotic regimen. From January 2001 to December 2013, 44 consecutive patients were diagnosed with EPN. The demographic characteristics, clinical presentations, management strategies, and final outcomes were analyzed retrospectively. The overall survival rate was 88.6% (39/44). Need for emergency hemodialysis, shock on initial presentation, altered mental status, severe hypoalbuminemia, inappropriate empirical antibiotic treatment and polymicrobial infections were significantly more common in the patients who died compared with the survivors. The overall failure rate of conservative treatment was 32.6% (14/43). Severe hypoalbuminemia (p = 0.003), need for emergency hemodialysis (p = 0.03), and polymicrobial infections (p = 0.04) were significantly associated with failure of conservative treatment. Severe hypoalbuminemia was independently associated with conservative management failure (p = 0.02). Even in the patients treated with percutaneous drainage plus effective antibiotics, failure was still associated with severe hypoalbuminemia (p = 0.01). According to the in vitro susceptibility data, third-generation cephalosporins is recommended as the empirical antibiotic regimen. Both appropriate empirical antibiotic and percutaneous drainage were essential for patients with EPN. Patients with severe hypoalbuminemia had a higher risk of conservative treatment failure, and additional management may be required.