The Role of Artificial Intelligence (AI) in Radiation Protection of Computed Tomography and Fluoroscopy: A Review  

作　　者：Emmanuel Worlali Fiagbedzi Philip Nii Gorleku Savanna Nyarko Vivian Della Atuwo-Ampoh Yawo Atsu Constantino Fiagan Adomako Asare Emmanuel Worlali Fiagbedzi;Philip Nii Gorleku;Savanna Nyarko;Vivian Della Atuwo-Ampoh;Yawo Atsu Constantino Fiagan;Adomako Asare(Uuniversity of Cape Coast, Department of Medical Imaging, Cape Coast, Ghana;Department of Medical Physics, University of Ghana, Accra, Ghana;University of Health and Allied Science, Ho, Ghana;Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium;Department of Radiology, Komfo Anokye Teaching Hospital, Kumasi, Ghana)

机构地区：[1]Uuniversity of Cape Coast, Department of Medical Imaging, Cape Coast, Ghana [2]Department of Medical Physics, University of Ghana, Accra, Ghana [3]University of Health and Allied Science, Ho, Ghana [4]Faculty of Medicine and Pharmacy, Vrije Universiteit Brussel, Brussels, Belgium [5]Department of Radiology, Komfo Anokye Teaching Hospital, Kumasi, Ghana

出　　处：《Open Journal of Medical Imaging》2022年第1期25-36,共12页医学影像期刊（英文）

摘　　要：Background: The medical imaging world is currently changing with the introduction of advanced modalities to help with diagnosis. There is then the need for the application of Artificial Intelligence (AI) in areas such as radiation protection to improve the safety as far as radiations are concerned. This review article discusses the principles, some of the challenges of radiation protection and the possible role of Artificial Intelligence (AI) regarding radiation protection in computed tomography and fluoroscopy exams. Methods: A literature search was done using Google Scholar, Science Direct and Pubmed to search for relevant articles concerning the review topic. Results: Some of the challenges identified were outdated and old X-ray machines, lack of QA programs on the machines amongst others. It was discovered that AI could be applied in areas like scan planning and positioning, patient positioning amongst others in CT imaging to reduce radiation doses. With fluoroscopy, an AI enabled system helped in reducing radiation doses by selecting the region of interest of pathology and exposing that region. Conclusion: The application of AI will improve safety and standards of practice in medical imaging.Background: The medical imaging world is currently changing with the introduction of advanced modalities to help with diagnosis. There is then the need for the application of Artificial Intelligence (AI) in areas such as radiation protection to improve the safety as far as radiations are concerned. This review article discusses the principles, some of the challenges of radiation protection and the possible role of Artificial Intelligence (AI) regarding radiation protection in computed tomography and fluoroscopy exams. Methods: A literature search was done using Google Scholar, Science Direct and Pubmed to search for relevant articles concerning the review topic. Results: Some of the challenges identified were outdated and old X-ray machines, lack of QA programs on the machines amongst others. It was discovered that AI could be applied in areas like scan planning and positioning, patient positioning amongst others in CT imaging to reduce radiation doses. With fluoroscopy, an AI enabled system helped in reducing radiation doses by selecting the region of interest of pathology and exposing that region. Conclusion: The application of AI will improve safety and standards of practice in medical imaging.

关 键 词：Artificial Intelligence (AI) Radiations Protection Computed Tomography FLUOROSCOPY 

分 类 号：R73[医药卫生—肿瘤]