Mohammed Aboud^1,, Shaimaa M Kadhim², Mustafa Radif³

Historically, classifications have relied primarily on the location of the urethral meatus. This oversimplifies the spectrum of the disease and does not account for factors such as urethral plate width, glans size, chordee factors such as urethral plate width, glans size, chordee severity, or penile torsion, all of which significantly impact surgical planning and outcomes. To overcome these shortcomings, this article introduces the Comprehensive Hypospadias Classification (CHC) and Surgical Complexity Score (SCS), using AI-aided classification and surgical decision-making. This 20-year retrospective cohort study was conducted at a single pediatric surgery unit and a private clinic in a low-resource environment. AI-assisted outcome tracking dashboards were used for longitudinal follow-up, optimizing patient management, and enhancing predictive algorithms. Descriptive statistics summed patient demographics and classification distribution. Interobserver agreement (Cohen's Kappa) was used to confirm surgeon 1 vs. Surgeon 2 (κ = 0.82 signifies substantial agreement). Surgeon 1 vs. AI (κ = 0.79 signifies high agreement). This study adheres to the TRIPOD guidelines to ensure the rigorous and standardized reporting of our AI-assisted hypospadias classification and risk prediction model. The structured case scenarios directly applied the study’s validated scoring and classification system for personalized hypospadias management; accordingly, these cases exemplify how our CHC-SCS system and AI-driven predictive models refine preoperative planning, surgical decision-making, and outcome prediction. With AI-powered surgical decision-making technology, the surgeons can enhance accuracy, reduce complications, and optimize hypospadias outcomes, and a new era in AI-powered pediatric urology has started.

Bridging AI, hypospadias, classification, Low resources, pediatric