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

Abstract

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.

Keywords: artificial intelligence; artificial intelligence applications; diagnostic accuracy; medical imaging; pediatric medicine; pediatric urology; predictive modeling; surgical challenge.

Figure 3

Anatomy and urine flow in healthy versus hydronephrosis kidneys [14].

Figure 4

Kidney ultrasound scans indicating different levels of hydronephrosis: (a) Grade 1, (b) Grade 2, (c) Grade 3, and (d) Grade 4. Based on SFU grading system [19].

Figure 5

Normal and obstructed ureteropelvic junction (Ariyanagam, 2024) [29].

Figure 6

Unenhanced abdominal CT images (kidney: yellow; kidney stone: green) [42].

Figure 7

Kidney CT image in grayscale (left) and with overlaid mask (right) to highlight suspected stones or tumors [46].

Figure 10

Anatomical characteristics used to classify urethral defects. UDR is urethral defect ratio; B-B is the imaginary line between glanular knobs (A) Key anatomical features of hypospadias. (B) Hypospadias severity grading [76].

Figure 11

Virtual reality surgical procedure [84].

Figure 1

AI for the precise identification of pediatric urology conditions.

Figure 2

Pediatric urological disorders discussed in this review.

Figure 8

Vesicoureteral reflux (VUR) grading.

Figure 9

Micturating cystourethrogram revealing the presence of type 1 PUV together with corresponding alterations in the bladder (trabeculation) and upper urinary system (vesicoureteral reflux) [63].