Purpose: This study aimed to develop and validate an AI-assisted framework for the automated evaluation of uroflowmetry data in patients presenting with lower urinary tract symptoms. The primary goal was to overcome the limitations of traditional manual interpretations by leveraging advanced machine learning techniques to achieve higher diagnostic accuracy, objectivity, and clinical applicability in urological assessments. Materials and methods: A retrospective analysis was conducted using a large, de-identified dataset comprising uroflowmetry recordings, patient-reported symptom scores, and comprehensive demographic data. The data underwent rigorous preprocessing—including noise reduction, baseline correction, normalization, and feature extraction—with key parameters such as peak flow rate, voided volume, average flow rate, and voiding time being analyzed. Multiple machine learning models—including a deep neural network, support vector machine, and random forest classifier—were developed and validated through cross-validation and extensive statistical testing. Performance metrics such as accuracy, sensitivity, specificity, and area under the ROC curve (AUC-ROC) were calculated, while multivariate regression analyses were performed to explore the relationships between uroflowmetry parameters and symptom severity. Results: The AI framework, particularly the deep neural network model, exhibited outstanding diagnostic performance with an accuracy of 92.5%, sensitivity of 90.0%, specificity of 94.0%, and an AUC-ROC of 0.96. Statistical analyses demonstrated significant correlations between key uroflowmetry parameters and clinical symptoms, with lower peak flow rates showing a strong association with increased symptom severity ( p < 0.001). These findings confirm that the integration of multi-dimensional data through AI significantly enhances the objectivity and precision of urinary function evaluation compared to conventional methods. Conclusion: The study successfully established an AI-assisted diagnostic framework that markedly improves the automated evaluation of uroflowmetry data and lower urinary tract symptoms. This innovative approach offers a robust alternative to traditional diagnostic practices by reducing subjectivity and enhancing diagnostic accuracy, thereby paving the way for more personalized and effective management of urinary disorders.

This study aimed to identify predictive factors for the formation of steinstrasse following extracorporeal shock wave lithotripsy (SWL) in patients with me

Background TRUS biopsy is the preferred method for diagnosing prostate cancer, but it can cause infectious complications that arise with the contribution of fluoroquinolone resistance. We aimed to explore the potential protective effect of a second rectal enema before biopsy. Methods Between January 2015 and December 2020, 419 patients were assessed retrospectively. Patients with a history of anticoagulant use, uncontrolled diabetes, urological surgery, prostate biopsy, or recent hospitalization or overseas travel, as well as those with previous prostatitis, were excluded from the study. The patients were subsequently divided into two groups: Group 1(n=223) had received one enema, on the morning of the biopsy, and Group 2(n=196) had received two, with the additional enema administered half an hour before the procedure. Results There was no significant difference between the groups in terms of age(p=0.076), BMI(p=0.489), diabetes(p=0.265), prostate-specific antigen(PSA) level(p=0.193), free/total PSA (f/t PSA) ratio(p=0.518), and prostate size(p=0.661) or in relation to cancer detection(p=0.428). The median hospitalization date was significantly higher in Group 1(p=0.003) as was urinary tract infection (UTI) development(p=0.004). However, there was no significant difference in terms of fever and sepsis(p=0.524andp=0.548,respectively). Additionally, subgroup analysis demonstrated that UTI was significantly lower in patients with diabetes mellitus who had received a second enema(p=0.004), though there was no significant difference in UTI between the groups in those without diabetes mellitus(p=0.215). Multivariable analysis showed that age and diabetes were significant risk factors for the development of UTI(OR:1.074,95%CI:1.027-1.130,p=0.002 and OR:1.220,95%CI:0.131-0.665,p=0.003, respectively). Furthermore, the second enema was a significant protective factor for preventing UTI(OR:-1.794,95%CI:2.208-16.389,p<0.001). Conclusion Older age and the presence of diabetes mellitus are independent risk factors for UTI after prostate biopsy. A second enema procedure before biopsy may protect patients from related infectious complications and could therefore be used as an alternative preventative method.