Artificial Intelligence in Pediatric Urology

Primary vesicoureteral reflux

Primary vesicoureteral reflux (VUR) is one of the most common urological abnormalities in infants and children. The association of VUR, urinary tract infection (UTI) and renal parenchymal damage is well established. The most serious complications of VUR-associated reflux nephropathy are hypertension and proteinuria with chronic kidney disease. Over the past two decades, our understanding of the natural history of VUR has improved, which has helped to identify patients at increased risk of both VUR and VUR-associated renal injury. The main goals in the treatment of paediatric patients with VUR are the prevention of recurrent UTIs and minimizing the risk of renal scarring and long-term renal impairment. Currently, there are four options for managing primary VUR in infants and children: surveillance or intermittent treatment of UTIs with management of bladder and bowel dysfunction; continuous antibiotic prophylaxis; endoscopic subureteral injection of tissue-augmenting substances; and ureteral reimplantation via open, laparoscopic or robotic-assisted surgery. Current debates regarding key aspects of management include when to perform diagnostic imaging and how to best identify the paediatric patients that will benefit from continuous antibiotic prophylaxis or surgical intervention, including endoscopic injection therapy and minimally invasive ureteral reimplantation. Evolving technologies, such as artificial intelligence, have the potential to assist clinicians in the decision-making process and in the individualization of diagnostic imaging and treatment of infants and children with VUR in the future.

Artificial Intelligence Tools in Pediatric Urology: A Comprehensive Review of Recent Advances

Artificial intelligence (AI) is providing novel answers to long-standing clinical problems, and it is quickly changing pediatric urology. This thorough analysis focuses on current developments in AI technologies that improve pediatric urology diagnosis, treatment planning, and surgery results. Deep learning algorithms help detect problems with previously unheard-of precision in disorders including hydronephrosis, pyeloplasty, and vesicoureteral reflux, where AI-powered prediction models have demonstrated promising outcomes in boosting diagnostic accuracy. AI-enhanced image processing methods have significantly improved the quality and interpretation of medical images. Examples of these methods are deep-learning-based segmentation and contrast limited adaptive histogram equalization (CLAHE). These methods guarantee higher precision in the identification and classification of pediatric urological disorders, and AI-driven ground truth construction approaches aid in the standardization of and improvement in training data, resulting in more resilient and consistent segmentation models. AI is being used for surgical support as well. AI-assisted navigation devices help with difficult operations like pyeloplasty by decreasing complications and increasing surgical accuracy. AI also helps with long-term patient monitoring, predictive analytics, and customized treatment strategies, all of which improve results for younger patients. However, there are practical, ethical, and legal issues with AI integration in pediatric urology that need to be carefully navigated. To close knowledge gaps, more investigation is required, especially in the areas of AI-driven surgical methods and standardized ground truth datasets for pediatric radiologic image segmentation. In the end, AI has the potential to completely transform pediatric urology by enhancing patient care, increasing the effectiveness of treatments, and spurring more advancements in this exciting area.

Prediction of spontaneous distal ureteral stone passage using artificial intelligence

PURPOSE

Identifying factors predicting the spontaneous passage of distal ureteral stones and evaluating the effectiveness of artificial intelligence in prediction.

MATERIALS AND METHODS

The files of patients presenting with distal ureteral stones were retrospectively evaluated. Those who experienced spontaneous passage were assigned to Group P, while those who did not were assigned to Group N. Demographic and clinical data of both groups were compared. Then, logistic regression analysis was performed to determine the factors predicting spontaneous stone passage. Based on these factors, a logistic regression model was prepared, and artificial intelligence algorithms trained on the dataset were compared with this model to evaluate the effectiveness of artificial intelligence in predicting spontaneous stone passage.

RESULTS

When comparing stone characteristics and NCCT findings, it was found that the stone size was significantly smaller in Group P (4.9 ± 1.7 mm vs. 6.8 ± 1.4 mm), while the ureteral diameter was significantly higher in Group P (3.3 ± 0.9 mm vs. 3.1 ± 1.1 mm) (p < 0.05). Parameters such as stone HU, stone radiopacity, renal pelvis AP diameter, and perirenal stranding were similar between the groups. In multivariate analysis, stone size and alpha-blocker usage were significant factors in predicting spontaneous stone passage. The ROC analysis for the logistic regression model constructed from the significant variables revealed an area under the curve (AUC) of 0.835, with sensitivity of 80.1% and specificity of 68.4%. AI algorithms predicted the spontaneous stone passage up to 92% sensitivity and up to 86% specifity.

CONCLUSIONS

AI algorithms are high-powered alternatives that can be used in the prediction of spontaneous distal ureteral stone passage.

The expanding role of artificial intelligence in the histopathological diagnosis in urological oncology: a literature review

The ongoing growth of artificial intelligence (AI) involves virtually every aspect of oncologic care in medicine. Although AI is in its infancy, it has shown great promise in the diagnosis of oncologic urological conditions. This paper aims to explore the expanding role of artificial intelligence in the histopathological diagnosis in urological oncology. We conducted a focused review of the literature on AI in urological oncology, searching PubMed and Google Scholar for recent advancements in histopathological diagnosis using AI. Various keyword combinations were used to find relevant sources published before April 2nd, 2024. We approached this article by focusing on the impact of AI on common urological malignancies by incorporating the use of different AI algorithms. We targeted the capabilities of AI's potential in aiding urologists and pathologists in histological cancer diagnosis. Promising results suggest AI can enhance diagnosis and personalized patient care, yet further refinements are needed before widespread hospital adoption. AI is transforming urological oncology by improving histopathological diagnosis and patient care. This review highlights AI's advancements in diagnosing prostate, renal cell, and bladder cancer. It is anticipated that as AI becomes more integrated into clinical practice, it will have a greater influence on diagnosis and improve patient outcomes.