Abstract
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.
Highlights
We address some of the issues by generating our own dataset through a welldesigned retrospective clinical study to augment the dataset available in public repositories
We considered a more realistic clinical deployment scenario and evaluated the performance of the models focusing on three key parameters: sensitivity (Sn), specificity (Sp), and positive predictive values (PPV)
Sn and Sp refer to the ability of the model to detect positive and negative cases, respectively, while PPV is the probability that the subjects with positive screening results are true COVID-19 positive
Summary
Several works, albeit adopting different base architectures and development strategies, have illustrated the potential of AI in detecting COVID-19 pneumonia using CXR images [4, 5, 9, 13, 14, 17,18,19,20,21]. From a methodical perspective, this study illustrates the potential of an AI-driven system for pneumonia (COVID-19, viral, and bacterial) detection considering a more realistic data distribution.
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