Artificial intelligence on COVID-19 pneumonia detection using chest xray images

• Published in: PloS one Vol. 16; no. 10; p. e0257884
• Main Authors: Baltazar, Lei Rigi, Manzanillo, Mojhune Gabriel, Gaudillo, Joverlyn, Viray, Ethel Dominique, Domingo, Mario, Tiangco, Beatrice, Albia, Jason
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 14.10.2021; Public Library of Science (PLoS)
• Subjects: Algorithms; Analysis; Artificial Intelligence; Biology and Life Sciences; Chest; Computer and Information Sciences; COVID-19; COVID-19 - diagnostic imaging; Datasets; Deep learning; Development strategies; Evaluation; Humans; Laboratories; Machine learning; Medical imaging; Medical imaging equipment; Medicine and Health Sciences; Modelling; People and Places; Pneumonia; Pneumonia - diagnostic imaging; Predictive Value of Tests; Radiography, Thoracic; Research and Analysis Methods; Robustness; Sensitivity and Specificity; Thorax - diagnostic imaging; Philippines
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0257884

Recent studies show the potential of artificial intelligence (AI) as a screening tool to detect COVID-19 pneumonia based on chest x-ray (CXR) images. However, issues on the datasets and study designs from medical and technical perspectives, as well as questions on the vulnerability and robustness of AI algorithms have emerged. In this study, we address these issues with a more realistic development of AI-driven COVID-19 pneumonia detection models by generating our own data through a retrospective clinical study to augment the dataset aggregated from external sources. We optimized five deep learning architectures, implemented development strategies by manipulating data distribution to quantitatively compare study designs, and introduced several detection scenarios to evaluate the robustness and diagnostic performance of the models. At the current level of data availability, the performance of the detection model depends on the hyperparameter tuning and has less dependency on the quantity of data. InceptionV3 attained the highest performance in distinguishing pneumonia from normal CXR in two-class detection scenario with sensitivity (Sn), specificity (Sp), and positive predictive value (PPV) of 96%. The models attained higher general performance of 91-96% Sn, 94-98% Sp, and 90-96% PPV in three-class compared to four-class detection scenario. InceptionV3 has the highest general performance with accuracy, F1-score, and g-mean of 96% in the three-class detection scenario. For COVID-19 pneumonia detection, InceptionV3 attained the highest performance with 86% Sn, 99% Sp, and 91% PPV with an AUC of 0.99 in distinguishing pneumonia from normal CXR. Its capability of differentiating COVID-19 pneumonia from normal and non-COVID-19 pneumonia attained 0.98 AUC and a micro-average of 0.99 for other classes.