Artificial intelligence in paediatric neuroradiology: current landscape, challenges, and future directions

Comprehensive Summary

This article is a review of the evolving role of artificial intelligence in pediatric and fetal neuroradiology, with a focus on the potential benefits of AI in the imaging of the developing brain. Pediatric neuroradiology is a field that is challenging because many neurological conditions in children are rare and the normal developmental changes in the brain make the interpretation of imaging studies more complicated than in the adult population. The authors discuss the evolving role of AI, including the application of machine learning and deep learning algorithms to the analysis of MRI and CT scans, which have the potential to assist with the automated segmentation of the brain, the detection of subtle abnormalities, the classification of neurological conditions, and the prediction of disease outcomes. The potential benefit of radiomics is also discussed, whereby features from the imaging studies could potentially correlate with clinical characteristics or disease biology; however, the authors also discuss the fact that the majority of AI studies are in the early research phase due to the lack of pediatric studies, the need for standardized data, and the requirement for validation across multiple institutions before this technology is available for widespread clinical practice.

Outcomes and Implications

Incorporation of AI in pediatric neuroradiology could bring about improved diagnostic capabilities and decision-making for neurological disorders in children. For example, the use of image analysis tools could aid radiologists in the detection of small abnormalities in the brain, such as small tumors, epileptogenic abnormalities, and brain malformations, which might otherwise be hard to detect. This could allow the physician to take necessary actions in the earliest stages of the disease and provide more individualized treatment plans for the patient. Another advantage of the use of AI in pediatric neuroradiology is the improvement of the overall workflow in the field. This could be achieved through the use of image segmentation, brain structure quantification, and disease prediction. This could lead to the improvement of the overall workflow in the field of neuroradiology. Furthermore, the use of optimized imaging protocols could minimize the use of ionizing radiation in the field of neuroradiology and improve the overall efficiency of MRI in the field. However, the use of AI in the field of pediatric neuroradiology is expected to be in the form of decision support tools for the physician.