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    Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review
    (Elsevier, 2023) Anastasiadis, Anastasios; Koudonas, Antonios; Langas, Georgios; Tsiakaras, Stavros; Memmos, Dimitrios; Mykoniatis, Ioannis; Symeonidis, Evangelos N.; Tsiptsios, Dimitrios; Savvides, Eliophotos; Vakalopoulos, Ioannis; Dimitriadis, Georgios; de la Rosette, Jean J. M. C. H.
    Objective: To provide a comprehensive review on the existing research and evi-dence regarding artificial intelligence (AI) applications in the assessment and management of urinary stone disease.Methods: A comprehensive literature review was performed using PubMed, Scopus, and Google Scholar databases to identify publications about innovative concepts or supporting applica-tions 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 publica-tions. The search was conducted from January 2000 to September 2023 and included manu-scripts in the English language.Results: 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.Conclusion: 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 alter-native or adjunct to the already existing data. However, little is known concerning the poten-tial 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. Never-theless, 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 dis-ease.