Catalyzing Precision Medicine: Artificial Intelligence Advancements in Prostate Cancer Diagnosis and Management.

BACKGROUND: The aim was to analyze the current state of deep learning (DL)-based prostate cancer (PCa) diagnosis with a focus on magnetic resonance (MR) prostate reconstruction; PCa detection/stratification/reconstruction; positron emission tomography/computed tomography (PET/CT); androgen deprivation therapy (ADT); prostate biopsy; associated challenges and their clinical implications. METHODS: A search of the PubMed database was conducted based on the inclusion and exclusion criteria for the use of DL methods within the abovementioned areas. RESULTS: A total of 784 articles were found, of which, 64 were included. Reconstruction of the prostate, the detection and stratification of prostate cancer, the reconstruction of prostate cancer, and diagnosis on PET/CT, ADT, and biopsy were analyzed in 21, 22, 6, 7, 2, and 6 studies, respectively. Among studies describing DL use for MR-based purposes, datasets with magnetic field power of 3 T, 1.5 T, and 3/1.5 T were used in 18/19/5, 0/1/0, and 3/2/1 studies, respectively, of 6/7 studies analyzing DL for PET/CT diagnosis which used data from a single institution. Among the radiotracers, [68Ga]Ga-PSMA-11, [18F]DCFPyl, and [18F]PSMA-1007 were used in 5, 1, and 1 study, respectively. Only two studies that analyzed DL in the context of DT met the inclusion criteria. Both were performed with a single-institution dataset with only manual labeling of training data. Three studies, each analyzing DL for prostate biopsy, were performed with single- and multi-institutional datasets. TeUS, TRUS, and MRI were used as input modalities in two, three, and one study, respectively. CONCLUSION: DL models in prostate cancer diagnosis show promise but are not yet ready for clinical use due to variability in methods, labels, and evaluation criteria. Conducting additional research while acknowledging all the limitations outlined is crucial for reinforcing the utility and effectiveness of DL-based models in clinical settings.

Focused UTUC pathways with a risk-stratified approach to diagnostic ureteroscopy: is it the need of the hour? A retrospective cohort analysis.

INTRODUCTION: Upper urinary tract urothelial cancer is a rare, aggressive variant of urinary tract cancer. There is often delay to diagnosis and management for this entity in view of diagnostic and staging challenges needing additional investigations and risk stratifications for improved outcomes. In this article, we share our experience in developing a dedicated diagnostic and treatment pathway for UTUC and assess its impact on time lines to radical nephroureterectomy (RNU). We also evaluate the impact of diagnostic ureteroscopy (DUR) on UTUC care pathways timelines. MATERIALS AND METHODS: A prospective database was maintained for all patients who underwent a RNU from January 2015 to August 2022 in a high-volume single tertiary care centre in the UK. In 2019, a Focused UTUC pathway (FUP) was implemented at the centre to streamline diagnostic and RNU pathways. A retrospective analysis of the database was conducted to compare time lines and diagnostic trends between the pre-FUP and FUP cohorts. Primary outcome measures were time to RNU from MDT. Secondary outcome measures were: impact of DUR on time to RNU from MDT and negative UTUC rates between DUR and non-DUR cohorts. Differences in continuous variables across categories were assessed using the independent sample t test. Categorical variables between cohorts were analysed using the chi-square (χ2). Statistical significance in this study was set as p < 0.05. RESULTS: A total of 500 patients with complete data were included in the analysis. The pre-FUP and FUP cohorts consisted of 313 patients and 187 patients, respectively. The overall cohort had a mean age (SD) of 70 years (9.3). 66% of the overall cohort were males. The median time to RNU from MDT in the FUP was significantly lower compared to the pre-FUP cohort; 62 days (IQR 59) vs. 48 days (IQR 41.5), p < 0.0001. The median time to RNU from MDT in patients who underwent a diagnostic URS in the FUP cohort was significantly lower compared to the pre-FUP cohort; 78.5 days (IQR 54.8) vs. 68 days (IQR 48), p-NS. The non-UTUC rates in the DUR and non-DUR cohorts were 6/248 (2.4%) and 14/251 (5.6%), respectively (NS). CONCLUSION: In this series, we illustrate the effectiveness of integrating a multidisciplinary approach with specialised personnel, ring-fenced clinics, efficient diagnostic assessment and optimised theatre capacity. By adopting a risk-stratified approach to diagnostic ureteroscopy, we have achieved a significant reduction in time to RNU.

Oncological Efficacy of Robotic Nephroureterectomy vs. Open and Laparoscopic Nephroureterectomy for Suspected Non-Metastatic UTUC-A Systematic Review and Meta-Analysis.

INTRODUCTION AND AIMS: The optimal approach for nephroureterectomy in patients with suspected UTUC remains a point of debate. In this review, we compare the oncological outcomes of robotic nephroureterectomy (RNU) with open (ONU) or laparoscopic nephroureterectomy (LNU). METHODS: All randomized trials and observational studies comparing RNU with ONU and/or LNU for suspected non-metastatic UTUC are included in this review. The systematic review was performed in accordance with the Cochrane Guidelines and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA). The primary outcome measures were overall survival (OS), cancer-specific survival (CSS), disease-free survival (DFS), and intravesical recurrence-free survival (IV-RFS). The secondary outcome measures were the lymph node dissection (LND) rates, positive margin rates, and the proportion of patients receiving bladder intravesical chemotherapy. RESULTS: We identified 8172 references through our electronic searches and 8 studies through manual searching. A total of 15 studies met the inclusion criteria. The total number of patients in the review was 18,964. RNU had superior OS compared to LNU (HR: 0.81 (95% CI: 0.71, 0.93), p-0.002 (very low certainty)). RNU and ONU had similar OS (HR: 0.83 (95% CI: 0.52, 1.34), p-0.44 (very low certainty)). One study reported an independent association of RNU as a worse predictor of IV-RFS when compared to ONU (HR-1.73 (95% CI: 1.22, 2.45)). The LND rates were higher in the RNU cohort when compared to the LNU cohort (RR 1.24 (95% CI: 1.03, 1.51), p-0.03 (low certainty)). The positive margin rate was lower in the RNU cohort when compared to the ONU cohort (RR 0.29 (95% CI: 0.08, 0.86), p-0.03 (low certainty)). CONCLUSION: RNU offers comparable oncological efficacy to ONU, except for intravesical recurrence-free survival (IV-RFS). RNU has fewer positive surgical margin rates compared to ONU in well-balanced studies. RNU appears to outperform LNU for certain oncological parameters, such as OS and the proportion of patients who receive lymph node dissections. The quality of evidence comparing surgical techniques for UTUC has remained poor in the last decade.

A Review of Modern Imaging Landscape for Prostate Cancer: A Comprehensive Clinical Guide.

The development of prostate cancer imaging is rapidly evolving, with many changes to the way patients are diagnosed, staged, and monitored for recurrence following treatment. New developments, including the potential role of imaging in screening and the combined diagnostic and therapeutic applications in the field of theranostics, are underway. In this paper, we aim to outline the current landscape in prostate cancer imaging and look to the future at the potential modalities and applications to come.

Role of Hounsfield Unit in Predicting Outcomes of Shock Wave Lithotripsy for Renal Calculi: Outcomes of a Systematic Review.

PURPOSE OF REVIEW: Extracorporeal shock wave lithotripsy success rates depend on several stone and patient-related factors, one of which is stone density which is calculated on computed tomography scan in Hounsfield Units. Studies have shown inverse correlation between SWL success and HU; however, there remains considerable variation between studies. We performed a systematic review regarding the use of HU in SWL for renal calculi to consolidate the current evidence and address current knowledge gaps. RECENT FINDINGS: Database including MEDLINE, EMBASE, and Scopus were searched from inception through August 2022. Studies in English language analysing stone density/attenuation in adult patients undergoing SWL for renal calculi were included for assessment of Shockwave lithotripsy outcomes, use of stone attenuation to predict success, use of mean and peak stone density and Hounsfield unit density, determination of optimum cut-off values, nomograms/scoring systems, and assessment of stone heterogeneity. 28 studies with a total of 4,206 patients were included in this systematic review with sample size ranging from 30 to 385 patients. Male to female ratio was 1.8, with an average age of 46.3 years. Mean overall ESWL success was 66.5%. Stone size ranged from 4 to 30 mm in diameter. Mean stone density was used by two-third of the studies to predict the appropriate cut-off for SWL success, ranging from 750 to 1000 HU. Additional factors such as peak HU and stone heterogeneity index were also evaluated with variable results. Stone heterogeneity index was considered a better indicator for success in larger stones (cut-off value of 213) and predicting SWL stone clearance in one session. Prediction scores had been attempted, with researchers looking into combining stone density with other factors such as skin to stone distance, stone volume, and differing heterogeneity indices with variable results. Numerous studies demonstrate a link between shockwave lithotripsy outcomes and stone density. Hounsfield unit < 750 has been found to be associated with shockwave lithotripsy success, with likelihood of failure strongly associated with values over 1000. Prospective standardisation of Hounsfield unit measurement and predictive algorithm for shockwave lithotripsy outcome should be considered to strengthen future evidence and help clinicians in the decision making. TRIAL REGISTRATION: International Prospective Register of Systematic Reviews (PROSPERO) database: CRD42020224647.

Role of three dimensional (3D) printing in endourology: An update from EAU young academic urologists (YAU) urolithiasis and endourology working group.

The management of nephrolithiasis has been complemented well by modern technological advancements like virtual reality, three-dimensional (3D) printing etc. In this review, we discuss the applications of 3D printing in treating stone disease using percutaneous nephrolithotomy (PCNL) and retrograde intrarenal surgery (RIRS). PCNL surgeries, when preceded by a training phase using a 3D printed model, aid surgeons to choose the proper course of action, which results in better procedural outcomes. The 3D printed models have also been extensively used to train junior residents and novice surgeons to improve their proficiency in the procedure. Such novel measures include different approaches employed to 3D print a model, from 3D printing the entire pelvicalyceal system with the surrounding tissues to 3D printing simple surgical guides.

Transforming healthcare through a digital revolution: A review of digital healthcare technologies and solutions.

The COVID-19 pandemic has put a strain on the entire global healthcare infrastructure. The pandemic has necessitated the re-invention, re-organization, and transformation of the healthcare system. The resurgence of new COVID-19 virus variants in several countries and the infection of a larger group of communities necessitate a rapid strategic shift. Governments, non-profit, and other healthcare organizations have all proposed various digital solutions. It's not clear whether these digital solutions are adaptable, functional, effective, or reliable. With the disease becoming more and more prevalent, many countries are looking for assistance and implementation of digital technologies to combat COVID-19. Digital health technologies for COVID-19 pandemic management, surveillance, contact tracing, diagnosis, treatment, and prevention will be discussed in this paper to ensure that healthcare is delivered effectively. Artificial Intelligence (AI), big data, telemedicine, robotic solutions, Internet of Things (IoT), digital platforms for communication (DC), computer vision, computer audition (CA), digital data management solutions (blockchain), digital imaging are premiering to assist healthcare workers (HCW's) with solutions that include case base surveillance, information dissemination, disinfection, and remote consultations, along with many other such interventions.

Current Applications of Artificial Intelligence in Benign Prostatic Hyperplasia.

Artificial intelligence is used in predicting the clinical outcomes before minimally invasive treatments for benign prostatic hyperplasia, to address the insufficient reliability despite multiple assessment parameters, such as flow rates and symptom scores. Various models of artificial intelligence and its contemporary applications in benign prostatic hyperplasia are reviewed and discussed. A search strategy adapted to identify and review the literature on the application of artificial intelligence with a dedicated search string with the following keywords: "Machine Learning," "Artificial Intelligence," AND "Benign Prostate Enlargement" OR "BPH" OR "Benign Prostatic Hyperplasia" was included and categorized. Review articles, editorial comments, and non-urologic studies were excluded. In the present review, 1600 patients were included from 4 studies that used different classifiers such as fuzzy systems, computer-based vision systems, and clinical data mining to study the applications of artificial intelligence in diagnoses and severity prediction and determine clinical factors responsible for treatment response in benign prostatic hyperplasia. The accuracy to correctly diagnose benign prostatic hyperplasia by Fuzzy systems was 90%, while that of computer-based vision system was 96.3%. Data mining achieved sensitivity and specificity of 70% and 50%, respectively, in correctly predicting the clinical response to medical treatment in benign prostatic hyperplasia. Artificial intelligence is gaining attraction in urology, with the potential to improve diagnostics and patient care. The results of artificial intelligence-based applications in benign prostatic hyperplasia are promising but lack generalizability of results. However, in the future, we will see a shift in the clinical paradigm as artificial intelligence applications will find their place in the guidelines and revolutionize the decision-making process.

Role of Deep Learning in Prostate Cancer Management: Past, Present and Future Based on a Comprehensive Literature Review.

This review aims to present the applications of deep learning (DL) in prostate cancer diagnosis and treatment. Computer vision is becoming an increasingly large part of our daily lives due to advancements in technology. These advancements in computational power have allowed more extensive and more complex DL models to be trained on large datasets. Urologists have found these technologies help them in their work, and many such models have been developed to aid in the identification, treatment and surgical practices in prostate cancer. This review will present a systematic outline and summary of these deep learning models and technologies used for prostate cancer management. A literature search was carried out for English language articles over the last two decades from 2000-2021, and present in Scopus, MEDLINE, Clinicaltrials.gov, Science Direct, Web of Science and Google Scholar. A total of 224 articles were identified on the initial search. After screening, 64 articles were identified as related to applications in urology, from which 24 articles were identified to be solely related to the diagnosis and treatment of prostate cancer. The constant improvement in DL models should drive more research focusing on deep learning applications. The focus should be on improving models to the stage where they are ready to be implemented in clinical practice. Future research should prioritize developing models that can train on encrypted images, allowing increased data sharing and accessibility.

Telemedicine and Telehealth in Urology: Uptake, Impact and Barriers to Clinical Adoption.

Telemedicine has great potential in urology as a strong medium for providing patients with continuous high-quality urological care despite the hurdles involved in its implementation. Both clinicians and patients are crucial factors in determining the success of tele-consults in terms of simplicity of use and overall satisfaction. For it to be successfully incorporated into routine urological practice, rigorous training and evidence-based recommendations are lacking. If these issues are addressed, they can provide a significant impetus for future tele-consults in urology and their successful deployment, even beyond the pandemic, to assure safer and more environment-friendly patient management.

Telemedicine and Telehealth in Urology-What Do the 'Patients' Think About It?

Telemedicine is the delivery of healthcare to patients who are not in the same location as the physician. The practice of telemedicine has a large number of advantages, including cost savings, low chances of nosocomial infection, and fewer hospital visits. Teleclinics have been reported to be successful in the post-surgery and post-cancer therapy follow-up, and in offering consulting services for urolithiasis patients. This review focuses on identifying the outcomes of the recent studies related to the usage of video consulting in urology centers for hematuria referrals and follow-up appointments for a variety of illnesses, including benign prostatic hyperplasia (BPH), kidney stone disease (KSD), and urinary tract infections (UTIs) and found that they are highly acceptable and satisfied. Certain medical disorders can cause embarrassment, social exclusion, and also poor self-esteem, all of which can negatively impair health-related quality-of-life. Telemedicine has proven beneficial in such patients and is a reliable, cost-effective patient-care tool, and it has been successfully implemented in various healthcare settings and specialties.

Legal and Ethical Consideration in Artificial Intelligence in Healthcare: Who Takes Responsibility?

The legal and ethical issues that confront society due to Artificial Intelligence (AI) include privacy and surveillance, bias or discrimination, and potentially the philosophical challenge is the role of human judgment. Concerns about newer digital technologies becoming a new source of inaccuracy and data breaches have arisen as a result of its use. Mistakes in the procedure or protocol in the field of healthcare can have devastating consequences for the patient who is the victim of the error. Because patients come into contact with physicians at moments in their lives when they are most vulnerable, it is crucial to remember this. Currently, there are no well-defined regulations in place to address the legal and ethical issues that may arise due to the use of artificial intelligence in healthcare settings. This review attempts to address these pertinent issues highlighting the need for algorithmic transparency, privacy, and protection of all the beneficiaries involved and cybersecurity of associated vulnerabilities.

Emergency upper urinary tract decompression: double-J stent or nephrostomy? A European YAU/ESUT/EULIS/BSIR survey among urologists and radiologists.

PURPOSE: To evaluate the decompression of the pelvicalyceal system between urologists and radiologists. METHODS: A survey was distributed to urologists and to radiologists comparing double-J stent (DJS), percutaneous nephrostomy (PN) and primary ureteroscopy (URS) for three clinical scenarios (1-febrile hydronephrosis; 2-obstruction and persistent pain; 3-obstruction and anuria) before and after reading literature The survey included perception on radiation dose, cost and quality of life (QoL). RESULTS: Response rate was 40% (366/915). 93% of radiologists believe that DJS offers a better QOL compared to 70.6% of urologists (p = 0.006). 28.4% of urologists consider PN to be more expensive compared to 8.9% of radiologists (p = 0.006). 75% of radiologists believe that radiation exposure is higher with DJS as opposed to 33.9% of urologists. There was not a difference in the decompression preference in the first scenario. After reading the literature, 28.6% of radiologists changed their opinion compared to 5.2% of urologists (p < 0.001). The change favored DJS. In the second scenario, responders preferred equally DJS and they did not change their opinion. In the third scenario, 41% of radiologists chose PN as opposed to 12.6% of urologists (p < 0.001). After reading the literature, 17.9% of radiologists changed their opinion compared to 17.9% of urologists (p < 0.001), in favor of DJS. Although the majority of urologists (63.4%) consistently perform primary URS, only 3, 37 and 21% preferred it for the first, second and third scenarios, respectively. CONCLUSION: The decision on the type of drainage of a stone-obstructing hydronephrosis should be individualized.

Attitudes and perceptions of outpatients towards adoption of telemedicine in healthcare during COVID-19 pandemic.

BACKGROUND: Asia is home to a burgeoning market for telemedicine with the availability of cheaper smartphones and internet services. Due to a rise in telemedicine use by doctors and patients, it is imperative to understand the perception of patients towards the adoption of telemedicine, the availability of telemedicine to the general population, the frequency with which patients avail these services, and the motivation or apprehensions in using them, especially during the COVID-19 pandemic. AIMS: The study is performed to understand the behavioral attitude and perceptions of the population regarding telemedicine and, in doing so, make services more user-friendly for patients. METHODS: A total of 1170 participants were surveyed using a structured online questionnaire to assess the perceptions towards the adoption of telemedicine in healthcare delivery services. Multivariate analysis was performed to identify key variables of knowledge and attitude affecting the utilization of telemedicine. RESULTS: Of the total respondents, 35.3% of patients never encountered telemedicine before and 26.9% did not come across telemedicine even during the COVID-19 pandemic. CONCLUSION: Understanding the perceptions of patients, using targeted health education, positive communication, and behavioral modifications, is the key factor to be addressed to mitigate the apprehensions towards telemedicine and improve the utilization of the services.

Ten-year survival outcomes after radical nephroureterectomy with a risk-stratified approach using prior diagnostic ureteroscopy: a single-institution observational retrospective cohort study.

OBJECTIVES: To evaluate the long-term oncological outcomes of patients with upper tract urothelial carcinoma (UTUC) undergoing radical nephroureterectomy (RNU) and the impact of diagnostic ureteroscopy (URS) on survival outcomes. MATERIALS AND METHODS: A retrospective analysis of all consecutive patients undergoing RNU for suspected UTUC at a UK tertiary referral centre from a prospectively maintained database was conducted. The primary outcome measures were 5- and 10-year cancer-specific survival (CSS). The secondary outcomes were: overall survival (OS), recurrence-free survival (RFS), impact of prior diagnostic URS on OS, CSS and intravesical RFS (intravesical-RFS), and predictors of intravesical recurrence. Statistical analysis was performed in R using the 'survminer' and 'survival' packages. The Kaplan-Meier method was used to calculate survival functions and these were expressed in graphical form. Uni-/multivariate survival analyses were performed using the Cox proportional hazard regression model. Statistical significance in this study was set at P < 0.05. RESULTS: A total of 422 patients underwent RNU with confirmed UTUC. The median (interquartile range) follow-up of patients with confirmed UTUC was 9.2 (5.6-12.7) years. The 5- and 10-year CSS rates were 70.5% (95% confidence interval [CI] 65.9-74.9) and 67.1% (95% CI 62.4-71.6), respectively. OS (HR 1.04 [95% CI 0.78-1.38]; P = 0.46) and CSS (HR 0.96 [95% CI 0.68-1.34]; P = 0.81) were similar in the diagnostic URS and the direct RNU cohorts. intravesical RFS was superior for the direct RNU cohort (HR 1.94 [95% CI 1.19-3.17]; P = 0.008). In multivariate analysis, prior URS, T2 stage, proximal ureter tumour and bladder cancer history were predictors of metachronous bladder recurrence. CONCLUSION: This single-centre retrospective cohort study reports the long-term oncological outcomes of RNU with a median follow-up of 9.2 years, serving as a reference standard in counselling patients undergoing RNU. Stage and grade of the RNU specimen were the only two studied factors that appeared to adversely impact long-term CSS and OS. Our results suggest that the risk of intravesical recurrence is increased nearly twofold in patients who have undergone diagnostic URS prior to RNU. Prior URS, however, does not appear to adversely impact long-term CSS and OS. The authors suggest that a risk-stratified approach be adopted, wherein diagnostic URS is offered only in equivocal cases.

The Ascent of Artificial Intelligence in Endourology: a Systematic Review Over the Last 2 Decades.

PURPOSE OF REVIEW: To highlight and review the application of artificial intelligence (AI) in kidney stone disease (KSD) for diagnostics, predicting procedural outcomes, stone passage, and recurrence rates. The systematic review was performed according to the Preferred Reporting Items for Systematic Reviews and Meta-analyses (PRISMA) checklist. RECENT FINDINGS: This review discusses the newer advancements in AI-driven management strategies, which holds great promise to provide an essential step for personalized patient care and improved decision making. AI has been used in all areas of KSD including diagnosis, for predicting treatment suitability and success, basic science, quality of life (QOL), and recurrence of stone disease. However, it is still a research-based tool and is not used universally in clinical practice. This could be due to a lack of data infrastructure needed to train the algorithms, wider applicability in all groups of patients, complexity of its use and cost involved with it. The constantly evolving literature and future research should focus more on QOL and the cost of KSD treatment and develop evidence-based AI algorithms that can be used universally, to guide urologists in the management of stone disease.

The history and evolution of 'webinars' and their role in urology: the modern way of training, education and communication.

The COVID-19 pandemic has had a significant impact on all domains of urology. Annual educational conferences by prominent urological bodies were suspended, due to the inability of conventional conference to adhere to social distancing stipulations. Innovative methods of healthcare delivery were therefore required to mitigate some of financial, health, training and research implications. Webinars is now a very popular method of communication and dissipation of knowledge with increasing adoption in medical education, training and also has been included in the curriculum of elite universities. The term 'webinar' is a combination of web and seminar, meaning a presentation, lecture, or workshop that is transmitted over the web. Webinars have proven to be convenient, flexible, cost-effective and reduce the carbon footprint. Furthermore, it is likely that webinars have a wider global audience reach and individual delegates have the ability to access more meetings from the comfort of their homes. The Urology community has been one of more prominent adopters of webinars in delivering educational activity during the pandemic. An estimated 400 urology webinars have been listed on DocMeetings since the onset of the COVID-19 pandemic. However, webinar is not without limitations. The didactic nature of webinars allows for minimal interpersonal interaction and constructive debate with the audience. It is however likely with potential technological advancements this going to be less of an issue in the future. It seems that the journey of webinars has just begun and will have an impact on training, education, communication and conferences for the foreseeable future.

Artificial Intelligence and Its Impact on Urological Diseases and Management: A Comprehensive Review of the Literature.

Recent advances in artificial intelligence (AI) have certainly had a significant impact on the healthcare industry. In urology, AI has been widely adopted to deal with numerous disorders, irrespective of their severity, extending from conditions such as benign prostate hyperplasia to critical illnesses such as urothelial and prostate cancer. In this article, we aim to discuss how algorithms and techniques of artificial intelligence are equipped in the field of urology to detect, treat, and estimate the outcomes of urological diseases. Furthermore, we explain the advantages that come from using AI over any existing traditional methods.

Big Data Analytics in urology: the story so far and the road ahead.

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.