Leaf-Specific Classification of Multi-Leaf Collimator Positioning Errors in Volumetric Modulated Arc Therapy Using a Convolutional Neural Network

Background/Objectives: Multi-leaf collimator (MLC) positioning accuracy critically affects delivered dose fidelity in volumetric modulated arc therapy (VMAT), yet conventional gamma-based quality assurance (QA) provides only plan-level pass/fail outcomes without leaf-specific error localization. This study developed and validated a convolutional neural network (CNN) framework that classifies the magnitude and direction of individual MLC leaf positioning errors directly from fluence map data. Met