Non-conventional diagnostic tools for lower urinary tract symptoms and bladder outlet obstruction in men: a perspective review.

Introduction Assessing male lower urinary tract symptoms (LUTS) due to Benign outlet obstruction (BOO) remains a challenge in urology due to the limitations of conventional diagnostic methods, which are often invasive, time-consuming, and inefficient. Objective Given these limitations, this review explores emerging non-conventional diagnostic approaches for evaluating benign male LUTS. Methods: A broad literature search was performed in November 2024 regarding the assessment of male LUTS exploiting tools different than UDS. The search strategy was implemented across Scopus, PubMed, and Web of Science.Results Ultrasonography, along with surrogate diagnostic methods such as detrusor wall thickness and intravesical prostatic protrusion, remains a key tool in the outpatient setting. Additionally, alternative methods, including near-infrared spectroscopy (NIRS), condom catheter testing, and penile cuff pressure analysis, are being investigated with the latter, showing potential as a non-invasive alternative to urodynamics, pending further future validations. Biomarkers, such as PSA, adiponectin, neural growth factor and miRNA are gaining interest in the scientific community as additional diagnostic frameworks to enhance diagnostic accuracy. Moreover, advancements in computational modeling and artificial intelligence (AI) are poised to revolutionize LUTS diagnostics. Computational modeling, though still in its early stages, offers valuable insights into anatomical and flow dynamics, providing objective parameters for assessing obstruction severity and the need for surgical intervention. Although it has not yet integrated in the current clinical practice, AI, in particular, may offer the potential to integrate diverse data sources, including diagnostic tests and patient-reported symptoms, to create more reliable predictive models for bladder outlet obstruction. Conclusion Given the rapid development and the large economic interest of machine learning, it is expected to play a pivotal role in the future of LUTS assessment, offering faster and more accurate diagnostic tools.