Abstract

Head trauma is a frequent cause of emergency department visits in children due to their larger head-to-body ratio and developing motor skills. Younger children, especially those under 2years, are at increased risk for skull fractures due to thinner cranial bones. Accurate diagnosis of pediatric skull fractures often requires imaging, including CT scans, which raise concerns about radiation exposure and its associated long-term risks, such as radiation-induced cancers. A recent study by Ono et al. highlights the necessity of follow-up imaging in children under 24months with isolated skull fractures (ISF) initially detected by CT. The study found that follow-up MRI scans revealed new intracranial findings in 40.6% of cases, with some requiring neurosurgical intervention. This underscores the importance of follow-up imaging in pediatric head trauma management. However, repeated imaging, particularly CT scans, increases radiation exposure, necessitating strategies to minimize it. Novel approaches like artificial intelligence (AI) are showing promise in reducing radiation doses by enhancing scan planning, patient positioning, and improving the quality of low-dose images. Despite the study's significant findings, limitations such as its retrospective design, potential selection bias, and single-institution setting restrict generalizability. Future research should adopt prospective designs, include broader case ranges, and involve multiple centers to validate findings. In conclusion, while timely imaging is crucial for managing pediatric head trauma, balancing diagnostic accuracy with reducing radiation exposure is essential. Advances in AI present promising avenues for enhancing safety in pediatric imaging.

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