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Purpose@#This clinical study sought to evaluate the possible clinical effectiveness and practicality of URINO, an innovative, incisionless, and disposable intravaginal device, designed for patients suffering from stress urinary incontinence. @*Methods@#A prospective, multicenter, single-arm clinical trial was carried out, involving women diagnosed with stress urinary incontinence who used a self-inserted, disposable intravaginal pessary device. Comparisons were made between the results of the 20-minute pad-weight gain (PWG) test at baseline and visit 3, where the device was applied. After 1 week of device usage, compliance, satisfaction, the sensation of a foreign body, and adverse events were assessed. @*Results@#Out of 45 participants, 39 completed the trial and expressed satisfaction within the modified intention-to-treat group. The average 20-minute PWG of participants was 17.2±33.6 g at baseline and significantly dropped to 5.3±16.2 g at visit 3 with device application. A total of 87.2% of participants exhibited a reduction ratio of PWG by 50% or more, surpassing the clinical trial success benchmark of 76%. The mean compliance was recorded as 76.6%±26.6%, the average visual analogue scale score for patient satisfaction was 6.4±2.6, and the sensation of a foreign body, measured on a 5-point Likert scale, was 3.1±1.2 after 1 week of device use. No serious adverse events were reported; there was 1 instance of microscopic hematuria and 2 cases of pyuria, all of which recovered. @*Conclusions@#The investigated device demonstrated significant clinical effectiveness and safety for patients with stress urinary incontinence. It was easy to use, showing favorable patient compliance. We propose that these disposable intravaginal pessaries could potentially be an alternative treatment for patients with stress urinary incontinence who are seeking nonsurgical options or are unable to undergo surgery.Trial Registration: The study was registered as a clinical trial (KCT0008369).
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PURPOSE: The aim of this study was to evaluate the applicability of machine learning methods that combine data on age and prostate-specific antigen (PSA) levels for predicting prostate cancer. MATERIALS AND METHODS: We analyzed 943 patients who underwent transrectal ultrasonography (TRUS)-guided prostate biopsy at Chungnam National University Hospital between 2014 and 2018 because of elevated PSA levels and/or abnormal digital rectal examination and/or TRUS findings. We retrospectively reviewed the patients’ medical records, analyzed the prediction rate of prostate cancer, and identified 20 feature importances that could be compared with biopsy results using 5 different algorithms, viz., logistic regression (LR), support vector machine, random forest (RF), extreme gradient boosting, and light gradient boosting machine. RESULTS: Overall, the cancer detection rate was 41.8%. In patients younger than 75 years and with a PSA level less than 20 ng/mL, the best prediction model for prostate cancer detection was RF among the machine learning methods based on LR analysis. The PSA density was the highest scored feature importances in the same patient group. CONCLUSIONS: These results suggest that the prediction rate of prostate cancer using machine learning methods not inferior to that using LR and that these methods may increase the detection rate for prostate cancer and reduce unnecessary prostate biopsy, as they take into consideration feature importances affecting the prediction rate for prostate cancer.