To compare the outcomes of open pyeloplasty (OP), laparoscopy-assisted extracorporeal (LEXP), and robotic-assisted laparoscopic pyeloplasty (RALP) for ureteropelvic junction obstruction in pediatric patients. We retrospectively reviewed the age-matched cohort of 30 children who underwent OP, 30 who underwent LEXP, and 10 who underwent RALP at a single institution, from 1996 to 2014. Pre- and post-operative variables including success rate were compared among surgical groups. The mean age of the patients was 120.2 months, the Society for Fetal Urology grade was 3.6, the anteroposterior diameter was 3.1 cm, and the renal relative function was 44.0%. The distribution of laterality, mean body mass index, and preoperative anteroposterior pelvic diameter on ultrasound did not differ among groups. The mean length of hospital stay was significantly shorter in the RALP group (3.2 days) than in the OP (6.6 days) and LEXP (5.8 days) groups (p<0.001). The duration of analgesics use was shorter in the RALP group (1.1 days) than in the other groups (p<0.001). During the mean follow-up period of 49.0, 20.1, and 16.6 months, the success rate was 96.7%, 89.7%, and 100% in the OP, LEXP, and RALP groups, respectively, although this difference was not statistically different (p = 0.499). In multivariate regression analysis, the presence of crossing vessels was the only factor that decreased the success rate (hazard ratio: 46.09, 95% confidence interval: 2.41–879.6, p = 0.011). Patients who undergo RALP have a reduced hospital stay and lower use of pain medication; however, there is no difference in the success rates for OP, LP, and RALP surgeries. The presence of crossing vessels is a negative prognostic indicator for surgical outcome regardless of the surgical method.

Purpose: This study aimed to evaluate the accuracy and reliability of a new smartphone-based acoustic uroflowmetry compared to conventional uroflowmetry.Methods: This was a prospective validation study enrolling 128 subjects from September 2017 to April 2018 comparing a novel acoustic uroflowmetry to conventional uroflowmetry in an outpatient urologic clinic at Seoul National University Bundang Hospital. Visual comparison of flow patterns and uroflow parameters such as maximum flow rate (Qmax), average flow rate (Qavg), and voided volume were compared between the 2 techniques. Reliability and accuracy of the uroflowmetry results were compared using Pearson correlation coefficient (PCC) and Student t-test, respectively.Results: One hundred twelve participants were included in the study. Of these, 77 had baseline urologic comorbidities while 35 were normal participants. Flow patterns between the 2 uroflowmetry techniques demonstrated strong visual correlation. When compared to conventional uroflowmetry, all 3 parameters of voiding in male participants showed a very robust correlation with PCC of 0.88, 0.91, and 0.95 for Qmax, Qavg, and voided volume, respectively. Among female participants, we observed a PCC of 0.78, 0.93, and 0.96 for Qmax, Qavg, and voided volume, respectively. The Qmax showed a statistically significant difference in both sexes between the 2 methods, although the absolute value was small.Conclusions: Uroflowmetry using acoustic analysis demonstrates comparable findings to conventional uroflowmetry. This provides an opportunity to perform uroflowmetry in the clinic or at home in a reliable, inexpensive manner. Future large-scale prospective studies are required to further validate our results.

Background: Noncontrast CT (NCCT) relies on labor-intensive examinations of CT slices to identify urolithiasis in the urinary tract, and, despite the use of deep-learning algorithms, false positives remain. Materials and Methods: A total of 410 NCCT axial scans from patients undergoing surgical treatment for urolithiasis were used for model development. The deep learning model was customized to combine a urolithiasis segmentation with per-slice classification for screening. Prediction models of the axial, coronal, and sagittal views were trained, and an additive model with an intersection of the coronal and sagittal predictions added to the axial outcome was introduced. Automated quantification of clinical metrics was evaluated in three-dimensional models of urinary stones. Results: The axial model detected 88.92% of urinary stones and produced a dice similarity coefficient of 87.56% in the urolithiasis segmentation. For urolithiasis (>5 mm), the sensitivity of the axial model reached 95.10%. False positives were reduced to 0.34 per patient using an ensemble of individual models. The additive model improved the sensitivity to 90.97% by detecting more small urolithiasis ( 0.964. Conclusions: The proposed system could reduce the burden on the physician for imaging diagnosis and help determine treatment strategies for urinary stones through automated quantification of clinical metrics with high accuracy and reproducibility.

Song SH, et al. Investig Clin Urol. 2022 Jul;63(4):455-463. https://doi.org/10.4111/icu.20220085