Automated detection of pneumonia in lung ultrasound using deep video classification for COVID-19. (2021)

• Record Type: Journal Article
• Authors: Erfanian Ebadi, Salehe; Krishnaswamy, Deepa; Bolouri, Seyed Ehsan Seyed; Zonoobi, Dornoosh; Greiner, Russell; Meuser-Herr, Nathaniel; Jaremko, Jacob L.; Kapur, Jeevesh; Noga, Michelle; Punithakumar, Kumaradevan
• Is Part Of: Informatics in medicine unlocked. Volume 25(2021)
• Publication Date: 2021
• Subjects: COVID-19 -- Lung ultrasound -- Video classification -- Convolutional neural networks; Medical informatics -- Periodicals; 610.285
• DOI: 10.1016/j.imu.2021.100687
• Languages: English
• ISSNs: 2352-9148

Abstract

Abstract: There is a crucial need for quick testing and diagnosis of patients during the COVID-19 pandemic. Lung ultrasound is an imaging modality that is cost-effective, widely accessible, and can be used to diagnose acute respiratory distress syndrome in patients with COVID-19. It can be used to find important characteristics in the images, including A-lines, B-lines, consolidation, and pleural effusion, which all inform the clinician in monitoring and diagnosing the disease. With the use of portable ultrasound transducers, lung ultrasound images can be easily acquired, however, the images are often of poor quality. They often require an expert clinician interpretation, which may be time-consuming and is highly subjective. We propose a method for fast and reliable interpretation of lung ultrasound images by use of deep learning, based on the Kinetics-I3D network. Our learned model can classify an entire lung ultrasound scan obtained at point-of-care, without requiring the use of preprocessing or a frame-by-frame analysis. We compare our video classifier against ground truth classification annotations provided by a set of expert radiologists and clinicians, which include A-lines, B-lines, consolidation, and pleural effusion. Our classification method achieves an accuracy of 90% and an average precision score of 95% with the use of 5-fold cross-validation. The results indicate the potential use of automated analysis of portable lung ultrasound images to assist clinicians in screening and diagnosing patients. Graphical abstract: Highlights: Automated detection of A-lines, B-lines and consolidation/pleural effusion. Evaluated over 1530 lung scans using 5-fold cross validation. Achieved an overall F1-score of 0.90.