To provide a comprehensive review on the existing research and evidence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease. A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applications of AI in the improvement of every medical procedure relating to stone disease. The terms ‘‘endourology’’, ‘‘artificial intelligence’’, ‘‘machine learning’’, and ‘‘urolithiasis'’ were used for searching eligible reports, while review articles, articles referring to automated procedures without AI application, and editorial comments were excluded from the final set of publications. The search was conducted from January 2000 to September 2023 and included manuscripts in the English language. A total of 69 studies were identified. The main subjects were related to the detection of urinary stones, the prediction of the outcome of conservative or operative management, the optimization of operative procedures, and the elucidation of the relation of urinary stone chemistry with various factors. AI represents a useful tool that provides urologists with numerous amenities, which explains the fact that it has gained ground in the pursuit of stone disease management perfection. The effectiveness of diagnosis and therapy can be increased by using it as an alternative or adjunct to the already existing data. However, little is known concerning the potential of this vast field. Electronic patient records, containing big data, offer AI the opportunity to develop and analyze more precise and efficient diagnostic and treatment algorithms. Nevertheless, the existing applications are not generalizable in real-life practice, and high-quality studies are needed to establish the integration of AI in the management of urinary stone disease.

Urolithiasis represents one of the most common urologic diseases, and its incidence demonstrates, globally, an increasing trend. The application of preventive measures is an established strategy to reduce urolithiasis-related morbidity, and it is based mostly on the adaptation of lifestyle factors and pharmacotherapy. Furthermore, other research areas demonstrate promising results, such as the research on the microbiome. In the current review, we searched for the latest data on lifestyle–based prevention and microbiome alterations in urolithiasis patients. The majority of the proposed lifestyle measures are already included in the urological guidelines, while additional factors, such as vitamin D supplementation, seem to have a putative positive effect. From the microbiome studies, several microbial composition patterns and metabolic pathways demonstrated an inhibiting or promoting role in lithogenesis. Up to the present, stone prevention has not shown satisfying results, which suggests that lifestyle measures are not adequate. Moreover, microbiome studies are prone to bias, since microbes are strongly affected by numerous clinical factors, while the analysis procedures are not standardized yet. Analysis standardization and data pooling from extensive registration of clinical and microbiome data are essential steps in order to improve the existing prevention strategy with targeted microbiome manipulations.

DNA methylation makes up a main part of the molecular mechanism of cancer evolution and has shown promising results in the prognosis of renal cell cancer (RCC). In this study, we investigated the possible association of promoter methylation of PCDH17, NEFH, RASSF1A, and FHIT, genes with the prognosis of nonmetastatic RCC patients. Cancerous and normal adjacent tissues from surgical specimens of 41 patients with long follow-up were treated for DNA isolation and bisulfite conversion. The gene promoter methylation was determined with quantitative methylation-specific PCR (qMSP). Wilcoxon signed-rank test was used for paired methylation comparisons, while univariate linear regression and Mann-Whitney test were applied for associating methylation status with clinical and disease characteristics. Cox regression proportional hazards models and Kaplan-Meier plots were used for survival analyses in reference to methylation status. Paired comparisons showed tissue-specific hypermethylation for PCDH17 (P < .001), NEFH (P < .001), RASSF1A (P = .032), while a positive association of methylation in normal tissues with age was demonstrated for PCDH17 (P < .001), RASSF1A (P < .001), FHIT (P < .001). PCDH17 was more methylated in cases with clear cell RCC (P = .015) and high-grade tumor (P = .013), while NEFH methylation was higher in locally advanced cases (P = .032). PCDH17 hypermethylation in cancerous and normal tissues was linked to shorter disease-specific survival (DSS, P = .026, P = .004), disease-free survival (DFS, P = .004, P = .019) while NEFH hypermethylation in cancerous tissues was related to shorter DSS (P = .032). Increased methylation difference of NEFH was also associated with shorter DSS (P = .041) and DFS (P = .020), while the corresponding parameter for PCDH17 was associated with poor DFS (P = .014). Kaplan-Meier curves for hypermethylation in cancer tissues demonstrated different clinical courses for PCDH17 (P = .017), NEFH (P = .023) regarding DSS, and PCDH17 (P = .001) regarding DFS. Our study not only highlights the prognostic value of promoter methylation of PCDH17 and NEFH in cancer tissues but also is the first report of the prognostic value of methylation alterations in normal tissues. Our findings are the first report of the prognostic value of methylation alterations in normal tissues, which can contribute to improved assessment of recurrence risk.