This study presents a novel, highly accurate CNN-based application for rapid detection of ductopenic parotid glands in sialo-CBCT images. The achieved performance metrics demonstrate the tool's potential to standardize ductopenia assessment and significantly improve clinical workflow efficiency. By providing an objective assessment of ductal arborization, this application offers valuable support for clinical decision-making and monitoring of glandular dysfunction. Future research directions should focus on multi-center validation studies and the integration of this AI application into routine clinical workflows to further establish its utility in diverse clinical settings.