Pediatric Radiology: Adapting Imaging Protocols for Younger Populations

Abstract

Background: Pediatric radiology is crucial in diagnosing and treating medical conditions in children, yet it poses unique challenges due to the heightened sensitivity of younger patients to ionizing radiation. The "as low as reasonably achievable" (ALARA) principle emphasizes minimizing radiation exposure while ensuring diagnostic efficacy.

Methods: This study systematically reviews literature on artificial intelligence (AI) applications for radiation dose optimization in pediatric imaging. An electronic search across multiple databases (PubMed, ScienceDirect, etc.) was conducted using keywords related to AI, dose reduction, and pediatrics, focusing on studies published after 2017.

Results: The review identified significant advancements in AI methodologies, particularly deep learning techniques, which have demonstrated potential in reducing radiation doses by 36% to 95% across various imaging modalities, including CT and PET scans. Most studies indicated that AI could maintain diagnostic image quality while significantly lowering radiation exposure, addressing both safety and efficacy concerns in pediatric radiology.

Conclusion: The findings underscore the importance of integrating AI-driven technologies in pediatric radiology to optimize radiation dose while ensuring high-quality imaging. Challenges remain, including the need for continuous education and standardization in pediatric imaging practices. Future research should focus on expanding the scope of studies to include a broader range of imaging modalities and larger sample sizes to validate AI applications comprehensively.