Asbestosis diagnosis algorithm combining the lung segmentation method and deep learning model in computed tomography image

Abstract

BackgroundEarly detection of asbestosis is important; hence, quick and accurate diagnostic tools are essential. This study aimed to develop an algorithm that combines lung segmentation and deep learning models that can be utilized as a clinical decision support system (CDSS) for diagnosing patients with asbestosis in segmented computed tomography (CT) images. MethodsWe accurately segmented the lungs in CT images of patients examined at Seoul St. Mary’s Hospital using a threshold-based method. Lungs with asbestosis and normal lungs were classified by applying the segmented image to the long-term recurrent convolutional network deep learning model. Performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and F1 score from the test data. ResultsThe algorithm developed using the DenseNet201pre-trained model showed excellent performance, with a sensitivity of 0.962, specificity of 0.975, accuracy of 0.970, AUROC of 0.968, and F1 score of 0.961. ConclusionsWe developed an algorithm with significantly better diagnostic accuracy than a radiologist (0.970 vs. 0.73–0.79). Our developed algorithm is expected to be an excellent support tool if used as a CDSS to diagnose asbestosis using CT images.