Research on real-time detection of radiotherapy setup errors and intelligent quality control methods based on artificial intelligence and big data

This study demonstrates the feasibility of applying unsupervised learning to identify statistically unusual setup patterns and proposes a closed-loop "setup-monitoring-alert" framework. The approach is intended as an auxiliary alerting tool to support clinical workflows, rather than to replace dosimetric evaluation or clinical decision-making.