Automated multi-lesion annotation in chest x-rays: Annotating over 450,000 images from public datasets using the AI-based Smart Imagery Framing and Truthing (SIFT) system

A newly proposed artificial intelligence (AI)-based tool, Smart Imagery Framing and Truthing (SIFT), was applied to provide lesion annotation of pulmonary abnormalities (or diseases) and their corresponding boundaries on 452,602 chest X-ray (CXR) images from four publicly available datasets. SIFT is based on Multi-task, Optimal-recommendation, and Max-predictive Classification and Segmentation (MOM ClaSeg) technologies to identify and delineate 65 different abnormalities. The MOM ClaSeg System is developed on a training dataset of over 300,000 CXR images, which contains over 240,000 confirmed abnormal images with over 300,000 confirmed ROIs corresponding to 65 different abnormalities and over 67,000 normal (i.e., “no finding”) images. SIFT system can determine the abnormality types of labeled ROIs and their boundary coordinates with high efficiency (improved 5.88 times) when radiologists used SIFT as an aide compared to radiologists using a traditional semi-automatic method. The SIFT system achieves an average sensitivity of 89.38%±11.46% across four datasets. This can be used to significantly improve the quality and quantity of training and testing sets to develop AI technologies.