Development of an AI-based Smart Imagery Framing and Truthing (SIFT) system to annotate pulmonary abnormalities with corresponding boundaries based on CT images

Advancements in artificial intelligence (AI) have improved the efficiency and accuracy of radiological examinations, but creating accurate AI models is still challenging due to the requirement for extensive manual annotation. We propose the Smart Imagery Framing and Truthing (SIFT) system to assist annotators in labeling and delineating pulmonary abnormalities on CT images. SIFT is developed on a training dataset of 9,078 CT images (18,367 ROIs) corresponding to 47 different abnormalities/diseases. An independent testing set with 2,199 CT images (4,280 ROIs) is processed by SIFT to predict both abnormality/disease types and ROI boundary locations. Evaluation metrics include IOU, AUC, and slice-level sensitivity of abnormality labels. For ROI segmentation, 91.5% and 36.2% of abnormalities had an IOU over 0.6 and 0.7, respectively. Regarding AUC, 97.9%, 80.9%, and 42.6% of abnormalities had values above 0.7, 0.8, and 0.9, respectively. For sensitivity, all 47 label categories exceeded 0.8. SIFT demonstrates high performance in determining abnormality/disease types with corresponding boundary locations for the ROIs, which can be used to predict training and testing sets to develop AI.