Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review

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dc.authorid0000-0002-6308-1763
dc.contributor.authorAnastasiadis, Anastasios
dc.contributor.authorKoudonas, Antonios
dc.contributor.authorLangas, Georgios
dc.contributor.authorTsiakaras, Stavros
dc.contributor.authorMemmos, Dimitrios
dc.contributor.authorMykoniatis, Ioannis
dc.contributor.authorSymeonidis, Evangelos N.
dc.contributor.authorTsiptsios, Dimitrios
dc.contributor.authorSavvides, Eliophotos
dc.contributor.authorVakalopoulos, Ioannis
dc.contributor.authorDimitriadis, Georgios
dc.contributor.authorde la Rosette, Jean J. M. C. H.
dc.date.accessioned2023-09-04T12:18:53Z
dc.date.available2023-09-04T12:18:53Z
dc.date.issued2023
dc.departmentİstanbul Medipol Üniversitesi, Tıp Fakültesi, Cerrahi Tıp Bilimleri Bölümü, Üroloji Ana Bilim Dalı
dc.description.abstractObjective: 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.
dc.description.sponsorshipCNN ; CNN2en_US
dc.identifier.citationAnastasiadis, A., Koudonas, A., Langas, G., Tsiakaras, S., Memmos, D., Mykoniatis, I. ... de la Rosette, J. J. M. C. H. (2023). Transforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review. Asian Journal of Urology, 10(3), 258-274. https://dx.doi.org/10.1016/j.ajur.2023.02.002
dc.identifier.doi10.1016/j.ajur.2023.02.002
dc.identifier.endpage274
dc.identifier.issn2214-3882
dc.identifier.issn2214-3890
dc.identifier.issue3
dc.identifier.pmid37538159
dc.identifier.scopus2-s2.0-85163386614
dc.identifier.scopusqualityQ2
dc.identifier.startpage258
dc.identifier.urihttps://dx.doi.org/10.1016/j.ajur.2023.02.002
dc.identifier.urihttps://hdl.handle.net/20.500.12511/11379
dc.identifier.volume10
dc.identifier.wos001047135900001en_US
dc.identifier.wosqualityN/A
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.institutionauthorde la Rosette, Jean J. M. C. H.
dc.language.isoen
dc.publisherElsevier
dc.relation.ispartofAsian Journal of Urologyen_US
dc.relation.publicationcategoryDiğer
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 International*
dc.rightsinfo:eu-repo/semantics/openAccess
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectArtificial Intelligence
dc.subjectEndourology
dc.subjectMachine Learning
dc.subjectStone Disease
dc.subjectUrolithiasis
dc.titleTransforming urinary stone disease management by artificial intelligence-based methods: A comprehensive review
dc.typeReview Article

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