This study aims to develop and validate an automated artificial intelligence (AI)-driven framework for the detection of pleural plaques (PP) presence from CT scans. To achieve this, an existing pre-trained network for PP segmentation from CT lung scans was integrated into a complete framework designed to detect the presence or absence of PP in individuals, thereby eliminating the need to retrain a large deep learning network. The proposed framework incorporates a novel, lightweight machine learning module that bridges the gap between PP segmentation and presence detection. The proposed framework was evaluated in a cohort of retired workers previously exposed to asbestos. The presence or absence of PP was assessed and compared to binary annotations from CT scans, which were manually labeled by three expert radiologists. The results highlighted the framework's potential in clinical scenarios where precise localization is unnecessary or where traditional segmentation models struggle due to the presence of small, fine lung structures.