Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment
PurposeThis 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.MethodsThe structure of the ANN model was designed considering...
| Main Authors: | , , , , , , , |
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| Format: | Article |
| Language: | English |
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Frontiers Media S.A.
2022-07-01
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| Series: | Frontiers in Public Health |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fpubh.2022.892789/full |
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| author | H. O. Tekin H. O. Tekin Faisal Almisned T. T. Erguzel Mohamed M. Abuzaid W. Elshami Antoaneta Ene Shams A. M. Issa Shams A. M. Issa Hesham M. H. Zakaly Hesham M. H. Zakaly |
| author_facet | H. O. Tekin H. O. Tekin Faisal Almisned T. T. Erguzel Mohamed M. Abuzaid W. Elshami Antoaneta Ene Shams A. M. Issa Shams A. M. Issa Hesham M. H. Zakaly Hesham M. H. Zakaly |
| author_sort | H. O. Tekin |
| collection | DOAJ |
| description | PurposeThis 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.MethodsThe 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.ResultsThe 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.ConclusionIt 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. |
| first_indexed | 2024-12-11T16:21:10Z |
| format | Article |
| id | doaj.art-fbccb7904f654eccafaafb82b9257d02 |
| institution | Directory Open Access Journal |
| issn | 2296-2565 |
| language | English |
| last_indexed | 2024-12-11T16:21:10Z |
| publishDate | 2022-07-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Public Health |
| spelling | doaj.art-fbccb7904f654eccafaafb82b9257d022022-12-22T00:58:51ZengFrontiers Media S.A.Frontiers in Public Health2296-25652022-07-011010.3389/fpubh.2022.892789892789Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessmentH. O. Tekin0H. O. Tekin1Faisal Almisned2T. T. Erguzel3Mohamed M. Abuzaid4W. Elshami5Antoaneta Ene6Shams A. M. Issa7Shams A. M. Issa8Hesham M. H. Zakaly9Hesham M. H. Zakaly10Department of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab EmiratesComputer Engineering Department, Faculty of Engineering and Natural Sciences, Istinye University, Istanbul, TurkeyDepartment Information Systems, College of Computer and Information Sciences, King Saud University, Riyadh, Saudi ArabiaDepartment of Software Engineering, Faculty of Engineering and Natural Sciences, Uskudar University, Istanbul, TurkeyDepartment of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Medical Diagnostic Imaging, College of Health Sciences, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Chemistry, Physics and Environment, Faculty of Sciences and Environment, INPOLDE Research Center, Dunarea de Jos University of Galati, Galati, RomaniaPhysics Department, Faculty of Science, Al-Azhar University, Assiut, EgyptPhysics Department, Faculty of Science, University of Tabuk, Tabuk, Saudi ArabiaPhysics Department, Faculty of Science, Al-Azhar University, Assiut, EgyptExperimental Physics Department, Institute of Physics and Technology, Ural Federal University, Ekaterinburg, RussiaPurposeThis 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.MethodsThe 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.ResultsThe 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.ConclusionIt 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.https://www.frontiersin.org/articles/10.3389/fpubh.2022.892789/fullartificial intelligence (AI)computed tomographyDLPabdominalartificial neural network (ANN) |
| spellingShingle | H. O. Tekin H. O. Tekin Faisal Almisned T. T. Erguzel Mohamed M. Abuzaid W. Elshami Antoaneta Ene Shams A. M. Issa Shams A. M. Issa Hesham M. H. Zakaly Hesham M. H. Zakaly Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment Frontiers in Public Health artificial intelligence (AI) computed tomography DLP abdominal artificial neural network (ANN) |
| title | Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment |
| title_full | Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment |
| title_fullStr | Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment |
| title_full_unstemmed | Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment |
| title_short | Utilization of artificial intelligence approach for prediction of DLP values for abdominal CT scans: A high accuracy estimation for risk assessment |
| title_sort | utilization of artificial intelligence approach for prediction of dlp values for abdominal ct scans a high accuracy estimation for risk assessment |
| topic | artificial intelligence (AI) computed tomography DLP abdominal artificial neural network (ANN) |
| url | https://www.frontiersin.org/articles/10.3389/fpubh.2022.892789/full |
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