Exploring the impact of artificial intelligence on radiation dose reduction in urological imaging: a systematic review from EAU endourology.

BACKGROUND: Patients with urological conditions often undergo recurrent computed tomography (CT) imaging, which results in cumulative radiation exposure which can be deleterious. Artificial intelligence (AI)- based technologies, including Deep Learning Image Reconstruction (DLIR), have emerged as a potential strategy to reduce radiation dose in CT imaging while maintaining image quality. This systematic review aimed to quantify the benefits of AI-based techniques in urological CT imaging. METHODS: A systematic search of Ovid MEDLINE, Embase, and Scopus was conducted. Studies assessing AI-based techniques in urological CT were included. Primary outcomes of interest were radiation dose metrics (CTDIvol, DLP, effective dose), with other outcomes of interest including image quality metrics along with diagnostic performance of AI techniques. RESULTS: Eleven studies met the inclusion criteria. All studies demonstrated a radiation dose reduction aided with AI techniques, with most reporting reductions of 60-80%. Consistently, Image quality was maintained or improved, with reduced image noise and increased signal-to-noise ratio. Limited evidence from diagnostic studies showed at-least comparable performance between AI-based reconstruction and conventional iterative reconstruction, but with unclear superiority at equivalent dose levels. CONCLUSION: AI-based techniques show a clear ability to allow radiation dose reduction in urological CT imaging, whilst maintaining or improving image quality. However, the current evidence base is limited by a lack of diagnostic outcomes, and further research into AI techniques is required in order to quantify their clinical effectiveness.