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Yıl 2023, Cilt: 7 Sayı: 2, 89 - 98, 20.06.2023

Adnan Karaıbrahımoglu Ümit Kara Özge Kılıçoğlu Yağmur Kara

Öz

Kaynakça

  • Kwee, T. C., & Kwee, R. M. (2020). Chest CT in COVID-19: what the radiologist needs to know. RadioGraphics, 40(7), 1848-1865. (https://doi.org/10.1148/rg.2020200159).
  • Griswold, D., Gempeler, A., Rosseau, G., Kaseje, N., Johnson, W. D., Kolias, A., et al (2021). Chest Computed Tomography for the Diagnosis of COVID-19 in Emergency Trauma Surgery Patients Who Require Urgent Care During the Pandemic: Protocol for an Umbrella Review. JMIR Research Protocols, 10(5), e25207.
  • Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., et al (2020). Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32-E40. (https://doi.org/10.1148/radiol.2020200642).
  • Karaböce, B., Çetin, E., Özdingiş, M., & Durmuş, H. O. (2017). Doku Benzeri Fantom İçindeki Farklı Cisimlerin Görüntü Ölçüm Doğrulama Çalışmaları. Tıp Teknolojileri Kongresi 17, 133–136.
  • Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019). Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 162–169. https://doi.org/10.1109/TRPMS.2018.2890359
  • Borghei, Y. S., Hosseinkhani, S., & Ganjali, M. R. (2022). “Plasmonic Nanomaterials”: An emerging avenue in biomedical and biomedical engineering opportunities. Journal of Advanced Research, 39, 61–71. https://doi.org/10.1016/j.jare.2021.11.006
  • Park, C., Took, C. C., & Seong, J. K. (2018). Machine learning in biomedical engineering. Biomedical Engineering Letters, 8(1), 1–3. https://doi.org/10.1007/s13534-018-0058-3
  • Ataseven, B. (2013). Yapay Sinir Ağları İle Öngörü Modellemesi. Öneri Dergisi 10(39):101-115.
  • Arı, A., Berberler, M.E. (2017). Yapay Sinir Ağları ile Tahmin ve Sınıflandırma Problemlerinin Çözümü İçin Arayüz Tasarımı. Acta Infologica 1(2):55-73.
  • Kwon, K., Kim, D. & Park, H. (2017). A parallel MR imaging method using multilayer perceptron. Med. Phys 44, 6209-6224. (https://doi.org/10.1002/mp.12600).
  • Balakrishnama, S., Ganapathiraju, A. (1998). Linear Discriminant Analysis - A Brief Tutorial. Report of Institute for Signal and Information Processing, Department of Electrical and Computer Engineering, Mississippi State University, 1-8.
  • Erhane, T.M., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V., Liu, H. (2018). Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sensing 10(4), 580. (https://doi.org/10.3390/rs10040580).
  • Biau, G., Scornet, E. (2016) A random forest guided tour. TEST 25(2):197-227. (Doi: 10.1007/s11749-016-0481-7)
  • Saritas, M.M., Yasar, A. (2019) Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering 7(2), 88-91. (https://doi.org/10.18201//ijisae.2019252786).
  • Solmaz, R., Günay, M., Alkan, A. (2020). Fonksiyonel Tiroit Hastalığı Tanısında Naive Bayes Sınıflandırıcının Kullanılması. Dicle üniversitesi Mühendislik Fakültesi Dergisi 11(3), 915-924. (https://doi.org/10.24012/dumf.687898).
  • Wu, Y., Duan, H., Du, S. (2015). Multiple fuzzy c-means clustering algorithm in medical diagnosis. Technol Health Care 23 Suppl 2, S519-27. (https://doi.org/10.3233/THC-150989).
  • Al-Augby, S., Majewski, S,. Majewska, A., Nermend, K. (2014). A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets. Folia Oeconomica Stetinensia 14(2), 19-36. (https://doi.org/10.1515/foli-2015-0001).
  • Wang, J., X. Duan, J. A. Christner, S. Leng, L. Yu, and C. H. McCollough CH. (2012). Attenuation-based estimation of patient size for the purpose of size-specific dose estimation in CT. Part I. Development and validation of methods using the CT image. Med. Phys. 39(11), 6764–71. (https://doi.org/10.1118/1.4754303).
  • Homayounieh, F., Holmberg, O., Umairi, R. A., Aly, S., Basevičius, A., Costa, P. R., et al (2021). Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 countries in the IAEA study. Radiology, 298(3), E141-E151. (https://doi.org/10.1148/radiol.2020203453).
  • Kalra, M. K., Homayounieh, F., Arru, C., Holmberg, O., & Vassileva, J. (2020). Chest CT practice and protocols for COVID-19 from a radiation dose management perspective. European Radiology, 30(12), 1-7.
  • Sharma, S., Kapadia, A., Fu, W., Abadi, E., Segars, W. P., & Samei, E. (2019). A real-time Monte Carlo tool for individualized dose estimations in clinical CT. Physics in Medicine & Biology, 64(21), 215020. (https://doi.org/10.1088 / 1361-6560 / ab467f).
  • Roser, P., Birkhold, A., Preuhs, A., Ochs, P., Stepina, E., Strobel, N., et al (2021). XDose: toward online cross-validation of experimental and computational X-ray dose estimation. International Journal of Computer Assisted Radiology and Surgery, 16(1), 1-10. (https://doi.org/10.1007 / s11548-020-02298-6).
  • Taylor, C. L. (2021). Implementation Strategies for Modeling and Simulation in Military Organizations. Walden Dissertations and Doctoral Studies. 10276. https://scholarworks.waldenu.edu/dissertations/10276
  • Smith-Bindman, R., Lipson, J., Marcus, R., Kim, K. P., Mahesh, M., Gould, R., Berrington de González, A., & Miglioretti, D. L. (2009). Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Archives of internal medicine 169(22), 2078–2086. (https://doi.org/10.1001/archinternmed.2009.427)
  • Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14, 4–15. https://doi.org/10.1109/RBME.2020.2987975
  • Dong, D., Tang, Z., Wang, S., Hui, H., Gong, L., Lu, Y., Xue, Z., Liao, H., Chen, F., Yang, F., Jin, R., Wang, K., Liu, Z., Wei, J., Mu, W., Zhang, H., Jiang, J., Tian, J., & Li, H. (2021). The Role of Imaging in the Detection and Management of COVID-19: A Review. IEEE Reviews in Biomedical Engineering, 14, 16–29. https://doi.org/10.1109/RBME.2020.2990959
  • İşbilir, F. , Kaynak, M. & Kesemen, M. A. A. (2018). Insulated Patient Transport Capsule for Chemical, Biological, Radiological and Nuclear (CBRN) Contamination Cases. European Mechanical Science, 2 (4), 133-139. DOI: 10.26701/ems.464243
  • Esme, U. (2017). A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic. European Mechanical Science, 1 (2), 47-55. DOI: 10.26701/ems.321194
  • Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019). ResUNet++: An Advanced Architecture for Medical Image Segmentation. Proceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019, 225–230. https://doi.org/10.1109/ISM46123.2019.00049
  • Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019). Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 162–169. https://doi.org/10.1109/TRPMS.2018.2890359
  • Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020). DoubleU-Net: A deep convolutional neural network for medical image segmentation. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2020-July, 558–564. https://doi.org/10.1109/CBMS49503.2020.00111

Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms

Yıl 2023, Cilt: 7 Sayı: 2, 89 - 98, 20.06.2023

Adnan Karaıbrahımoglu Ümit Kara Özge Kılıçoğlu Yağmur Kara

Öz

Objective: The aim of the study is to predict the absorbed radiation dose on thorax CT imaging in geriatric patients with COVID-19.
Materials and Method: The SIEMENS SENSATION 64 CT scanner was performed with real protocols to patients (male/female phantom) using Monte Carlo simulation methods with the patient’s real height and weight nts and the actual parameters CT scanner. Absorbed organ doses have been calculated based on these Monte Carlo results. These results were used to predict the optimal absorbed radiation dose by Artificial Neural Network, Linear Discriminant Analysis, Random Forest Classification, and Naive-Bayes Classification algorithms. The dose values were clustered for genders by the Fuzzy C-Means algorithm.
Results: The ages of the patients were between 60 and 70 years. The Body Mass Indexes of male and female patients were 26.11 ± 4.49 and 25.03 ± 4.86 kg/m2 respectively. All classification algorithms were validated with approximately 100% success. The Fuzzy C-Means technique was found to be successful in clustering the dose values for gender clusters.
Conclusion: While the predicted and the observed values of patients do not change in the organs/tissues around and outside of the thorax, they generally vary in the intra-thoracic organs and tissues. It can be concluded that data-driven techniques are useful to obtain optimal radiation doses for organs/tissues in CT imaging.

Anahtar Kelimeler

Biomedical engineering Medical imaging Radiation dose COVID-19 Classification

Kaynakça

  • Kwee, T. C., & Kwee, R. M. (2020). Chest CT in COVID-19: what the radiologist needs to know. RadioGraphics, 40(7), 1848-1865. (https://doi.org/10.1148/rg.2020200159).
  • Griswold, D., Gempeler, A., Rosseau, G., Kaseje, N., Johnson, W. D., Kolias, A., et al (2021). Chest Computed Tomography for the Diagnosis of COVID-19 in Emergency Trauma Surgery Patients Who Require Urgent Care During the Pandemic: Protocol for an Umbrella Review. JMIR Research Protocols, 10(5), e25207.
  • Ai, T., Yang, Z., Hou, H., Zhan, C., Chen, C., Lv, W., et al (2020). Correlation of chest CT and RT-PCR testing for coronavirus disease 2019 (COVID-19) in China: a report of 1014 cases. Radiology 296(2), E32-E40. (https://doi.org/10.1148/radiol.2020200642).
  • Karaböce, B., Çetin, E., Özdingiş, M., & Durmuş, H. O. (2017). Doku Benzeri Fantom İçindeki Farklı Cisimlerin Görüntü Ölçüm Doğrulama Çalışmaları. Tıp Teknolojileri Kongresi 17, 133–136.
  • Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019). Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 162–169. https://doi.org/10.1109/TRPMS.2018.2890359
  • Borghei, Y. S., Hosseinkhani, S., & Ganjali, M. R. (2022). “Plasmonic Nanomaterials”: An emerging avenue in biomedical and biomedical engineering opportunities. Journal of Advanced Research, 39, 61–71. https://doi.org/10.1016/j.jare.2021.11.006
  • Park, C., Took, C. C., & Seong, J. K. (2018). Machine learning in biomedical engineering. Biomedical Engineering Letters, 8(1), 1–3. https://doi.org/10.1007/s13534-018-0058-3
  • Ataseven, B. (2013). Yapay Sinir Ağları İle Öngörü Modellemesi. Öneri Dergisi 10(39):101-115.
  • Arı, A., Berberler, M.E. (2017). Yapay Sinir Ağları ile Tahmin ve Sınıflandırma Problemlerinin Çözümü İçin Arayüz Tasarımı. Acta Infologica 1(2):55-73.
  • Kwon, K., Kim, D. & Park, H. (2017). A parallel MR imaging method using multilayer perceptron. Med. Phys 44, 6209-6224. (https://doi.org/10.1002/mp.12600).
  • Balakrishnama, S., Ganapathiraju, A. (1998). Linear Discriminant Analysis - A Brief Tutorial. Report of Institute for Signal and Information Processing, Department of Electrical and Computer Engineering, Mississippi State University, 1-8.
  • Erhane, T.M., Lane, C.R., Wu, Q., Autrey, B.C., Anenkhonov, O.A., Chepinoga, V.V., Liu, H. (2018). Decision-Tree, Rule-Based, and Random Forest Classification of High-Resolution Multispectral Imagery for Wetland Mapping and Inventory. Remote Sensing 10(4), 580. (https://doi.org/10.3390/rs10040580).
  • Biau, G., Scornet, E. (2016) A random forest guided tour. TEST 25(2):197-227. (Doi: 10.1007/s11749-016-0481-7)
  • Saritas, M.M., Yasar, A. (2019) Performance Analysis of ANN and Naive Bayes Classification Algorithm for Data Classification. International Journal of Intelligent Systems and Applications in Engineering 7(2), 88-91. (https://doi.org/10.18201//ijisae.2019252786).
  • Solmaz, R., Günay, M., Alkan, A. (2020). Fonksiyonel Tiroit Hastalığı Tanısında Naive Bayes Sınıflandırıcının Kullanılması. Dicle üniversitesi Mühendislik Fakültesi Dergisi 11(3), 915-924. (https://doi.org/10.24012/dumf.687898).
  • Wu, Y., Duan, H., Du, S. (2015). Multiple fuzzy c-means clustering algorithm in medical diagnosis. Technol Health Care 23 Suppl 2, S519-27. (https://doi.org/10.3233/THC-150989).
  • Al-Augby, S., Majewski, S,. Majewska, A., Nermend, K. (2014). A Comparison Of K-Means And Fuzzy C-Means Clustering Methods For A Sample Of Gulf Cooperation Council Stock Markets. Folia Oeconomica Stetinensia 14(2), 19-36. (https://doi.org/10.1515/foli-2015-0001).
  • Wang, J., X. Duan, J. A. Christner, S. Leng, L. Yu, and C. H. McCollough CH. (2012). Attenuation-based estimation of patient size for the purpose of size-specific dose estimation in CT. Part I. Development and validation of methods using the CT image. Med. Phys. 39(11), 6764–71. (https://doi.org/10.1118/1.4754303).
  • Homayounieh, F., Holmberg, O., Umairi, R. A., Aly, S., Basevičius, A., Costa, P. R., et al (2021). Variations in CT Utilization, Protocols, and Radiation Doses in COVID-19 Pneumonia: Results from 28 countries in the IAEA study. Radiology, 298(3), E141-E151. (https://doi.org/10.1148/radiol.2020203453).
  • Kalra, M. K., Homayounieh, F., Arru, C., Holmberg, O., & Vassileva, J. (2020). Chest CT practice and protocols for COVID-19 from a radiation dose management perspective. European Radiology, 30(12), 1-7.
  • Sharma, S., Kapadia, A., Fu, W., Abadi, E., Segars, W. P., & Samei, E. (2019). A real-time Monte Carlo tool for individualized dose estimations in clinical CT. Physics in Medicine & Biology, 64(21), 215020. (https://doi.org/10.1088 / 1361-6560 / ab467f).
  • Roser, P., Birkhold, A., Preuhs, A., Ochs, P., Stepina, E., Strobel, N., et al (2021). XDose: toward online cross-validation of experimental and computational X-ray dose estimation. International Journal of Computer Assisted Radiology and Surgery, 16(1), 1-10. (https://doi.org/10.1007 / s11548-020-02298-6).
  • Taylor, C. L. (2021). Implementation Strategies for Modeling and Simulation in Military Organizations. Walden Dissertations and Doctoral Studies. 10276. https://scholarworks.waldenu.edu/dissertations/10276
  • Smith-Bindman, R., Lipson, J., Marcus, R., Kim, K. P., Mahesh, M., Gould, R., Berrington de González, A., & Miglioretti, D. L. (2009). Radiation dose associated with common computed tomography examinations and the associated lifetime attributable risk of cancer. Archives of internal medicine 169(22), 2078–2086. (https://doi.org/10.1001/archinternmed.2009.427)
  • Shi, F., Wang, J., Shi, J., Wu, Z., Wang, Q., Tang, Z., He, K., Shi, Y., & Shen, D. (2021). Review of Artificial Intelligence Techniques in Imaging Data Acquisition, Segmentation, and Diagnosis for COVID-19. IEEE Reviews in Biomedical Engineering, 14, 4–15. https://doi.org/10.1109/RBME.2020.2987975
  • Dong, D., Tang, Z., Wang, S., Hui, H., Gong, L., Lu, Y., Xue, Z., Liao, H., Chen, F., Yang, F., Jin, R., Wang, K., Liu, Z., Wei, J., Mu, W., Zhang, H., Jiang, J., Tian, J., & Li, H. (2021). The Role of Imaging in the Detection and Management of COVID-19: A Review. IEEE Reviews in Biomedical Engineering, 14, 16–29. https://doi.org/10.1109/RBME.2020.2990959
  • İşbilir, F. , Kaynak, M. & Kesemen, M. A. A. (2018). Insulated Patient Transport Capsule for Chemical, Biological, Radiological and Nuclear (CBRN) Contamination Cases. European Mechanical Science, 2 (4), 133-139. DOI: 10.26701/ems.464243
  • Esme, U. (2017). A hybrid approach for the prediction and optimization of cutting forces using grey-based fuzzy logic. European Mechanical Science, 1 (2), 47-55. DOI: 10.26701/ems.321194
  • Jha, D., Smedsrud, P. H., Riegler, M. A., Johansen, D., De Lange, T., Halvorsen, P., & Johansen, H. D. (2019). ResUNet++: An Advanced Architecture for Medical Image Segmentation. Proceedings - 2019 IEEE International Symposium on Multimedia, ISM 2019, 225–230. https://doi.org/10.1109/ISM46123.2019.00049
  • Guo, Z., Li, X., Huang, H., Guo, N., & Li, Q. (2019). Deep learning-based image segmentation on multimodal medical imaging. IEEE Transactions on Radiation and Plasma Medical Sciences, 3(2), 162–169. https://doi.org/10.1109/TRPMS.2018.2890359
  • Jha, D., Riegler, M. A., Johansen, D., Halvorsen, P., & Johansen, H. D. (2020). DoubleU-Net: A deep convolutional neural network for medical image segmentation. Proceedings - IEEE Symposium on Computer-Based Medical Systems, 2020-July, 558–564. https://doi.org/10.1109/CBMS49503.2020.00111
Toplam 31 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Mühendislik
Bölüm Research Article
Yazarlar

Adnan Karaıbrahımoglu 0000-0002-8277-0281

Ümit Kara 0000-0002-1435-9810

Özge Kılıçoğlu 0000-0002-8443-9816

Yağmur Kara 0000-0003-4117-0455

Yayımlanma Tarihi 20 Haziran 2023
Kabul Tarihi 20 Mayıs 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 7 Sayı: 2

Kaynak Göster

APA Karaıbrahımoglu, A., Kara, Ü., Kılıçoğlu, Ö., Kara, Y. (2023). Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms. European Mechanical Science, 7(2), 89-98.
AMA Karaıbrahımoglu A, Kara Ü, Kılıçoğlu Ö, Kara Y. Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms. EMS. Haziran 2023;7(2):89-98.
Chicago Karaıbrahımoglu, Adnan, Ümit Kara, Özge Kılıçoğlu, ve Yağmur Kara. “Prediction of Absorption Dose of Radiation on Thorax CT Imaging in Geriatric Patients With COVID-19 by Classification Algorithms”. European Mechanical Science 7, sy. 2 (Haziran 2023): 89-98.
EndNote Karaıbrahımoglu A, Kara Ü, Kılıçoğlu Ö, Kara Y (01 Haziran 2023) Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms. European Mechanical Science 7 2 89–98.
IEEE A. Karaıbrahımoglu, Ü. Kara, Ö. Kılıçoğlu, ve Y. Kara, “Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms”, EMS, c. 7, sy. 2, ss. 89–98, 2023.
ISNAD Karaıbrahımoglu, Adnan vd. “Prediction of Absorption Dose of Radiation on Thorax CT Imaging in Geriatric Patients With COVID-19 by Classification Algorithms”. European Mechanical Science 7/2 (Haziran 2023), 89-98.
JAMA Karaıbrahımoglu A, Kara Ü, Kılıçoğlu Ö, Kara Y. Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms. EMS. 2023;7:89–98.
MLA Karaıbrahımoglu, Adnan vd. “Prediction of Absorption Dose of Radiation on Thorax CT Imaging in Geriatric Patients With COVID-19 by Classification Algorithms”. European Mechanical Science, c. 7, sy. 2, 2023, ss. 89-98.
Vancouver Karaıbrahımoglu A, Kara Ü, Kılıçoğlu Ö, Kara Y. Prediction of absorption dose of radiation on Thorax CT imaging in geriatric patients with COVID-19 by classification algorithms. EMS. 2023;7(2):89-98.
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