Artificial Intelligence for Radiation Dose Optimization in Pediatric Radiology: A Systematic Review

Abstract

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.

Diagnostic Performance of Artificial Intelligence-Based Computer-Aided Detection and Diagnosis in Pediatric Radiology: A Systematic Review

“…This systematic review of the diagnostic performance of the AI-based CAD in pediatric radiology was conducted as per the preferred reporting items for systematic reviews and meta-analyses (PRISMA) guidelines and patient/population, intervention, comparison, and outcome model. This involved a literature search, article selection, and data extraction and synthesis [ 10 , 12 , 13 , 14 , 18 ].…”

“…A reviewer with more than 20 years of experience in conducting literature reviews was involved in the article selection process [ 14 , 24 ]. Only peer-reviewed original research articles that were written in English and focused on the AI-based CAD in pediatric radiology with the diagnostic accuracy measures were included.…”

“…Pediatric radiology is a subset of radiology [ 14 , 15 , 16 , 17 ]. The aforementioned systematic review findings may not be applicable to the pediatric radiology [ 10 , 12 , 13 , 16 , 17 ].…”

Convergence of Artificial Intelligence and Neuroscience towards the Diagnosis of Neurological Disorders—A Scoping Review

“…Dose optimization—as discussed in [ 89 ], AI is being used in every stage of CT imaging to obtain high-quality images and help reduce noise and optimize radiation dosage [ 90 ]. Moreover, AI-based methods have found application in predicting radiation dosages, as described in [ 91 ].…”

Artificial Intelligence in Emergency Radiology: Where Are We Going?

“…Pediatric radiology departments could significantly benefit from AI-based dose reduction systems. In a recent systematic review, Curtise et al explored different applications of DL reconstruction algorithms in pediatric patients: their key conclusion was that DL-assisted image reconstructions could provide the radiologist with optimal image quality and an overall dose reduction of 30–80% [ 28 ].…”