Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review
Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has bee...
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| Format: | Journal Article |
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MDPI
2022
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| Online Access: | https://www.mdpi.com/journal/children http://hdl.handle.net/20.500.11937/88895 |
| _version_ | 1848765105892753408 |
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| author | Ng, Curtise |
| author2 | Tsiflikas, Ilias |
| author_facet | Tsiflikas, Ilias Ng, Curtise |
| author_sort | Ng, Curtise |
| building | Curtin Institutional Repository |
| collection | Online Access |
| description | Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies. |
| first_indexed | 2025-11-14T11:29:58Z |
| format | Journal Article |
| id | curtin-20.500.11937-88895 |
| institution | Curtin University Malaysia |
| institution_category | Local University |
| last_indexed | 2025-11-14T11:29:58Z |
| publishDate | 2022 |
| publisher | MDPI |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | curtin-20.500.11937-888952022-07-27T04:45:33Z Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review Ng, Curtise Tsiflikas, Ilias as low as reasonably achievable computed tomography convolutional neural network deep learning dose reduction generative adversarial network image processing machine learning medical imaging noise Radiation dose optimization is particularly important in pediatric radiology, as children are more susceptible to potential harmful effects of ionizing radiation. However, only one narrative review about artificial intelligence (AI) for dose optimization in pediatric computed tomography (CT) has been published yet. The purpose of this systematic review is to answer the question “What are the AI techniques and architectures introduced in pediatric radiology for dose optimization, their specific application areas, and performances?” Literature search with use of electronic databases was conducted on 3 June 2022. Sixteen articles that met selection criteria were included. The included studies showed deep convolutional neural network (CNN) was the most common AI technique and architecture used for dose optimization in pediatric radiology. All but three included studies evaluated AI performance in dose optimization of abdomen, chest, head, neck, and pelvis CT; CT angiography; and dual-energy CT through deep learning image reconstruction. Most studies demonstrated that AI could reduce radiation dose by 36–70% without losing diagnostic information. Despite the dominance of commercially available AI models based on deep CNN with promising outcomes, homegrown models could provide comparable performances. Future exploration of AI value for dose optimization in pediatric radiology is necessary due to small sample sizes and narrow scopes (only three modalities, CT, positron emission tomography/magnetic resonance imaging and mobile radiography, and not all examination types covered) of existing studies. 2022 Journal Article http://hdl.handle.net/20.500.11937/88895 10.3390/children9071044 https://www.mdpi.com/journal/children http://creativecommons.org/licenses/by/4.0/ MDPI fulltext |
| spellingShingle | as low as reasonably achievable computed tomography convolutional neural network deep learning dose reduction generative adversarial network image processing machine learning medical imaging noise Ng, Curtise Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title | Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title_full | Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title_fullStr | Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title_full_unstemmed | Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title_short | Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review |
| title_sort | artificial intelligence for radiation dose optimization in pediatric radiology: a systematic review |
| topic | as low as reasonably achievable computed tomography convolutional neural network deep learning dose reduction generative adversarial network image processing machine learning medical imaging noise |
| url | https://www.mdpi.com/journal/children http://hdl.handle.net/20.500.11937/88895 |