Artificial intelligence in paediatric head trauma: enhancing diagnostic accuracy for skull fractures and brain haemorrhages.

Pediatric head trauma is a significant cause of morbidity and mortality, with children, particularly those under two years old, being more susceptible to skull fractures due to their unique physiological and developmental characteristics. A recent study by Azusa Ono et al. examined the impact of repeated imaging in children under 24 months with minor head trauma, revealing that 40.6% of those who underwent follow-up MRI after an initial CT scan showed new intracranial findings. The study emphasizes the importance of careful consideration of repeated imaging based on initial findings and associated risk factors, such as the presence of subcutaneous hematoma and fractures intersecting coronal sutures. This underscores the need for improved diagnostic approaches to minimize radiation exposure while ensuring accurate diagnosis.Artificial Intelligence (AI) offers a promising solution, with research indicating that AI models can significantly improve diagnostic precision, increasing accuracy from 78.1 to 85.2% and reducing errors by two to three times. Additionally, AI has demonstrated high accuracy in detecting various types of brain hemorrhages, potentially facilitating earlier and more precise detection of hematomas associated with skull fractures. Integrating AI into diagnostic practices could enhance early detection, reduce diagnostic errors, and improve outcomes for pediatric head trauma cases. The study underscores the critical need for advanced diagnostic methods to better manage and treat head injuries in young children, where timely and accurate diagnosis is crucial.