Deep learning model-assisted detection of kidney stones on computed tomography

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dc.authoridCaglayan, Alper/0000-0001-6884-489X
dc.authoridKOCADURDU, KENAN/0000-0003-4274-6188
dc.authoridHORSANALI, Mustafa Ozan/0000-0002-3651-0948
dc.authorwosidCaglayan, Alper/AAC-9373-2022
dc.authorwosidKOCADURDU, KENAN/HJJ-1462-2023
dc.authorwosidHORSANALI, Mustafa Ozan/AAU-2142-2021
dc.contributor.authorÇaglayan, Alper
dc.contributor.authorHorsanalı, Mustafa Ozan
dc.contributor.authorKocadurdu, Kenan
dc.contributor.authorİsmailoğlu, Eren
dc.contributor.authorGüneyli, Serkan
dc.date.accessioned2023-03-22T19:47:24Z
dc.date.available2023-03-22T19:47:24Z
dc.date.issued2022
dc.departmentBelirleneceken_US
dc.description.abstractIntroduction: The aim of this study was to investigate the success of a deep learning model in detecting kidney stones in different planes according to stone size on unenhanced computed tomography (CT) images. Materials and Methods: This retrospective study included 455 patients who underwent CT scanning for kidney stones between January 2016 and January 2020; of them, 405 were diagnosed with kidney stones and 50 were not. Patients with renal stones of 0-1 cm, 1-2 cm, and >2 cm in size were classified into groups 1, 2, and 3, respectively. Two radiologists reviewed 2,959 CT images of 455 patients in three planes. Subsequently, these CT images were evaluated using a deep learning model. The accuracy rate, sensitivity, specificity, and positive and negative predictive values of the deep learning model were determined. Results: The training group accuracy rates of the deep learning model were 98.2%, 99.1%, and 97.3% in the axial plane; 99.1%, 98.2%, and 97.3% in the coronal plane; and 98.2%, 98.2%, and 98.2% in the sagittal plane, respectively. The testing group accuracy rates of the deep learning model were 78%, 68% and 70% in the axial plane; 63%, 72%, and 64% in the coronal plane; and 85%, 89%, and 93% in the sagittal plane, respectively. Conclusions: The use of deep learning algorithms for the detection of kidney stones is reliable and effective. Additionally, these algorithms can reduce the reporting time and cost of CT-dependent urolithiasis detection, leading to early diagnosis and management.en_US
dc.identifier.doi10.1590/S1677-5538.IBJU.2022.0132
dc.identifier.endpage839en_US
dc.identifier.issn1677-5538
dc.identifier.issn1677-6119
dc.identifier.issue5en_US
dc.identifier.pmid35838509en_US
dc.identifier.scopus2-s2.0-85134632038en_US
dc.identifier.scopusqualityQ2en_US
dc.identifier.startpage830en_US
dc.identifier.urihttps://doi.org/10.1590/S1677-5538.IBJU.2022.0132
dc.identifier.urihttps://hdl.handle.net/20.500.14034/680
dc.identifier.volume48en_US
dc.identifier.wosWOS:000829081700013en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.language.isoenen_US
dc.publisherBrazilian Soc Urolen_US
dc.relation.journalInternational Braz J Urolen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/openAccessen_US
dc.subjectKidney Calculien_US
dc.subjectTomography, X-Ray Computeden_US
dc.subjectAlgorithmsen_US
dc.subjectArtificial Intelligenceen_US
dc.subjectDiseaseen_US
dc.titleDeep learning model-assisted detection of kidney stones on computed tomographyen_US
dc.typeArticleen_US

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