Purpose of review The aim of this review is to provide an overview of surgical treatment options for male infertility including varicocelectomy, treatment of ejaculatory duct obstruction, vasovasostomy, and sperm extraction, and to review recent advances in techniques and technologies that may improve operative outcomes. Recent findings Microscopic subinguinal varicocelectomy has been shown to have the highest success rates with lowest rates of complications, and may be facilitated by the use of Doppler, indocyanine green angiography, and the 4K3D operating video microscope. The standard treatment for ejaculatory duct obstruction by transurethral resection of the ejaculatory ducts has changed little over time, but vesiculoscopy may allow for temporary dilation of an obstruction to allow for natural conception, while also offering diagnostic capabilities. Use of the robotic platform has gained popularity for vasectomy reversals but controversy remains regarding the cost-effectiveness of this option. Recently, a reinforcing suture technique has been described for vasovasostomy to minimize anastomotic breakdown and reversal failure. Finally, gray-scale and color-enhanced ultrasound may improve ability to predict successful sperm retrieval during extraction procedures. Summary Though the fundamentals of surgical treatment options for male infertility have changed little with time, technological advancements have contributed to improved surgical outcomes over recent years.

Introduction The COVID-19 pandemic has caused wide-reaching change to many aspects of life on a worldwide scale. The impact of these changes on peer-reviewed research journals, including those dedicated to urology, is still unknown. Material and methods The Web of Science database was queried to retrieve all COVID-19 urological articles written in English language and published between January 1 st , 2020 and December 10 th , 2021. Only original and review articles were considered. A bibliometric analysis of the total number of papers, citations, institutions and publishing journals was performed. Non-COVID-19 publications were also retrieved to compare the duration of publication stages. Results A total of 428 COVID-19 articles and 14,874 non-COVID-19 articles were collected. Significant differences in the duration of all the publication stages were found between COVID-19 and non-COVID-19 articles (all p <0.001). The most productive countries were the USA (100 articles), Italy (59 articles) and the United Kingdom (55 articles). The published literature has focused on four topics: COVID-19 genitourinary manifestations, management of urological diseases during the pandemic, repercussions on quality of life and impact on healthcare providers. Conclusions A significant reduction in peer review time for COVID-19 articles might raise concerns regarding the quality of peer review itself. USA, Italy and UK published the highest number of COVID-19 related articles. Restrictive measures taken by governments to reduce the spread of infection had a strong impact on mental stress and anxiety of patients and healthcare professionals. A coerced deferral of diagnosis and treatment of emergencies and uro-oncological cases represented the most challenging task from a clinical standpoint.

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center with accessible pre-operative MRI images. We developed a novel deep learning algorithm using U-Net architecture to identify kidneys on T2-weighted MRI and quantify non-neoplastic renal parenchymal volume (RV). The cohort was divided into a 74/13/13% split of training/validation/test subsets. Model development was carried out using a 5-fold cross validation technique. An ensemble of the three best performing models on the training and validation subsets was implemented to generate a more robust prediction segmentation. The associations between height-normalized preoperative RV and PORF were assessed using generalized linear mixed effect models, adjusted for known clinical factors associated with PORF (age, diabetes, preoperative eGFR, proteinuria, tumor size, time from surgery).RESULTS: MRI images from from 330 patients, including 208 PN and 122 RN were used to develop a deep learning algorithm with a final Dice coefficient of 0.93 and Jaccard index of 0.87 compared to manual segmentations (Figure 1). On unadjusted analyses, RV was associated with PORF following PN and RN (p <0.001 and p[0.008, respectively). When added to existing multivariable models to predict PORF, the associations between RV and PORF remained statistically significant (p <0.001 and p[0.05, respectively).CONCLUSIONS: Pre-operative non-neoplastic renal volume is associated with long-term renal function following PN and RN, even after adjusting for a previously validated clinical prediction model. We developed a deep learning tool to facilitate automated RV assessment, which may promote integration of RV measurement into clinical practice.