AI-Driven Automated Detection of Pleural Plaques on Chest CT Scans in Retired Asbestos-Exposed Workers

Background/Objective: This study aimed to develop and validate an automated framework for detecting pleural plaques (PPs) at the patient level using a single chest CT scan. Methods: A database of chest CT scans with corresponding individual binary annotations for PP presence was first established through expert visual assessment, based on consensus among a board of specialized radiologists. This dataset served to train and validate a patient-level classification framework for automated PP presence detection. This framework leverages a pre-trained PP segmentation network, thus obviating the need for retraining large-scale deep learning models. This segmentation backbone is supplemented by a lightweight classification module that integrates the network's outputs to infer the presence or absence of PPs at patient level. Furthermore, a patch-wise processing strategy was employed to optimize computational efficiency and training time while retaining critical local contextual information. Results: The framework was evaluated on a cohort of 1241 retired workers with documented occupational asbestos exposure, using 10-fold cross-validation (30% training, 10% validation, 60% testing). It achieved a balanced accuracy of 97.0%, sensitivity of 96.6%, and specificity of 97.4%, demonstrating performance slightly superior to that of two expert radiologists (balanced accuracy: 90.9%/91.4%, sensitivity: 88.0%/88.8%, specificity: 93.9%/94.1% for Expert1/Expert2) and substantially superior to that of a radiologist without specialized expertise in asbestos-related manifestations (balanced accuracy: 80.0%, sensitivity: 75.1%, specificity: 84.8%). Conclusion: The proposed framework demonstrates strong potential for automated patient-level identification of pleural plaques from a single CT scan. Our approach could be particularly useful in screening settings that involve compensation claims related to occupational diseases.