Advances in urethral stricture diagnostics and urethral reconstruction beyond traditional imaging: a scoping review

Introduction Urethral stricture disease is considered one of the more functionally bothersome aspects of urological conditions. The management of such disease is also traditionally managed with urethroplasty, or in severe cases, reconstruction. With the rise of artificial intelligence (AI) playing its part in diagnostics and treatment of urological conditions, we sought to determine its use case in urethral conditions in today’s era of advanced surgical care. Material and methods A comprehensive literature search was performed to identify literature on advances in diagnosis and management of urethral strictures. Publications in English were selected, whilst studies that were case reports, abstracts only, reviews, or conference posters were excluded. Results Twelve studies were finalised for review. Conventional neural networks and computational fluid dynamics implemented in retrograde urethrography reduced false positive and negative rates of urethral stricture diagnosis. Four-detector row computed tomography and magnetic resonance imaging voiding with virtual urethroscopy are also emerging imaging combination options for identification, offering decreased duration needed for diagnosis and increased correlation with intraoperative findings of urethral stricturing. For tissue re-engineering for urethral strictures, the role of 3-dimensional bioprinting of both autologous and allogenic sources has been on the rise, with promising findings of sustained tissue viability demonstrated in several in vitro animal studies and showing potential for expansion into human utilisation. Conclusions Advances in detection and management of urethral strictures have steadily been increasing its capacity, especially with the rise in artificial AI-driven learning algorithms and more accurate objectivity. Further studies are awaited to validate the use case of AI models in fields of urethral stricturing disease.