Salivary gland dysfunction presents significant diagnostic challenges in clinical practice, with ductopenia emerging as a crucial structural indicator of glandular impairment [1]. Characterized by reduced ductal arborization, ductopenia has been associated with reduced salivary flow and increased inflammatory markers [2]. While sialo cone-beam CT (sialo-CBCT) enables detailed visualization of ductal architecture, the interpretation of these images remains largely subjective and time-consuming [3]. The primary objective of this study was to develop and validate an artificial intelligence (AI) application for automated detection of parotid gland ductopenia using sialo-CBCT images. This aimed to establish a standardized approach for objective ductopenia assessment and improve diagnostic efficiency in clinical settings [4].