Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment

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dc.authoridHüseyin Ozan Tekin / 0000-0002-0997-3488en_US
dc.authorscopusidHüseyin Ozan Tekin / 56971130700
dc.authorwosidHüseyin Ozan Tekin / J-9611-2016en_US
dc.contributor.authorTekin, Hüseyin Ozan
dc.contributor.authorAlmisned, Faisal
dc.contributor.authorErgüzel, Türker Tekin
dc.contributor.authorAbuzaid, Mohamed M.
dc.contributor.authorEne, Antoaneta
dc.contributor.authorZakaly, Hesham M.
dc.contributor.authorElshami, Wiam
dc.date.accessioned2022-08-19T05:23:54Z
dc.date.available2022-08-19T05:23:54Z
dc.date.issued2022en_US
dc.departmentİstinye Üniversitesi, Mühendislik ve Doğa Bilimleri Fakültesi, Bilgisayar Mühendisliği Bölümüen_US
dc.description.abstractPurpose: This study aimed to evaluate Artificial Neural Network (ANN) modeling to estimate the significant dose length product (DLP) value during the abdominal CT examinations for quality assurance in a retrospective, cross-sectional study. Methods: The structure of the ANN model was designed considering various input parameters, namely patient weight, patient size, body mass index, mean CTDI volume, scanning length, kVp, mAs, exposure time per rotation, and pitch factor. The aforementioned examination details of 551 abdominal CT scans were used as retrospective data. Different types of learning algorithms such as Levenberg-Marquardt, Bayesian and Scaled-Conjugate Gradient were checked in terms of the accuracy of the training data. Results: The R-value representing the correlation coefficient for the real system and system output is given as 0.925, 0.785, and 0.854 for the Levenberg-Marquardt, Bayesian, and Scaled-Conjugate Gradient algorithms, respectively. The findings showed that the Levenberg-Marquardt algorithm comprehensively detects DLP values for abdominal CT examinations. It can be a helpful approach to simplify CT quality assurance. Conclusion: It can be concluded that outcomes of this novel artificial intelligence method can be used for high accuracy DLP estimations before the abdominal CT examinations, where the radiation-related risk factors are high or risk evaluation of multiple CT scans is needed for patients in terms of ALARA. Likewise, it can be concluded that artificial learning methods are powerful tools and can be used for different types of radiation-related risk assessments for quality assurance in diagnostic radiology.en_US
dc.identifier.citationTekin HO, Almisned F, Erguzel TT, Abuzaid MM, Elshami W, Ene A, Issa SAM, Zakaly HMH. Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment. Front Public Health. 2022 Jul 28;10:892789. doi: 10.3389/fpubh.2022.892789. PMID: 35968466; PMCID: PMC9366721.en_US
dc.identifier.doi10.3389/fpubh.2022.892789en_US
dc.identifier.issn2296-2565en_US
dc.identifier.pmid9366721en_US
dc.identifier.scopus2-s2.0-85135957771en_US
dc.identifier.scopusqualityQ1en_US
dc.identifier.urihttp://doi.org/10.3389/fpubh.2022.892789
dc.identifier.urihttps://hdl.handle.net/20.500.12713/3106
dc.identifier.wosWOS:000861293600001en_US
dc.identifier.wosqualityQ1en_US
dc.indekslendigikaynakWeb of Scienceen_US
dc.indekslendigikaynakScopusen_US
dc.indekslendigikaynakPubMeden_US
dc.institutionauthorTekin, Hüseyin Ozan
dc.language.isoenen_US
dc.publisherFrontiersen_US
dc.relation.ispartofFrontiers in Public Healthen_US
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanıen_US
dc.rightsinfo:eu-repo/semantics/closedAccessen_US
dc.subjectDLPen_US
dc.subjectAbdominalen_US
dc.subjectArtificial Intelligence (AI)en_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectComputed Tomographyen_US
dc.titleUtilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessmenten_US
dc.typeArticleen_US

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