Tekin, Hüseyin OzanAlmisned, FaisalErgüzel, Türker TekinAbuzaid, Mohamed M.Ene, AntoanetaZakaly, Hesham M.Elshami, Wiam2022-08-192022-08-192022Tekin 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: PMC9366721.2296-2565http://doi.org/10.3389/fpubh.2022.892789https://hdl.handle.net/20.500.12713/3106Purpose: 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.eninfo:eu-repo/semantics/closedAccessDLPAbdominalArtificial Intelligence (AI)Artificial Neural Network (ANN)Computed TomographyArticle9366721WOS:0008612936000012-s2.0-85135957771Q110.3389/fpubh.2022.892789Q1