Performance of Commercial Deep Learning-Based Radiation Dose Optimization Software in Pediatric Radiology: A...

Background/Objectives: To date, no systematic review has focused specifically on commercially available deep learning-based image reconstruction (DLIR) software for radiation dose optimization in pediatric radiology. The purpose of this article was to systematically review original clinical studies evaluating the performance of commercial DLIR software for radiation dose optimization in pediatric radiology and to assess their methodological quality. Methods: A literature search was conducted on 28 April 2026 using seven electronic databases. The review was registered with the Open Science Framework (Registration DOI: 10.17605/OSF.IO/3JDQS). Results: Thirteen papers met the selection criteria and were included in the review. These studies evaluated five commercial computed tomography (CT) DLIR software products. Clinically achievable radiation dose reductions ranged from 11.2% to 97.9% without compromising image quality, and in some cases, even exceeding that of the reference standard, suggesting further dose reduction potential. Excluding two studies that reported substantially lower dose reductions, the clinically achievable dose reduction range was 36.0-97.9%. Furthermore, more than three-quarters of the included articles reported dose reductions of at least 50% while maintaining image quality. Improvements in study methodology were evident among papers published from 2025 onward. Conclusions: Commercial CT DLIR software can achieve substantial radiation dose reductions in pediatric CT while maintaining image quality. However, further clinical studies are needed to evaluate a broader range of commercial DLIR software, including applications in positron emission tomography and X-ray imaging. Future studies should ideally include the full pediatric age range, prospectively collected and adequately sized datasets from underrepresented geographic regions, effective dose assessment, and clinically meaningful outcome measures such as diagnostic confidence/quality/accuracy.