End-to-end automatic differentiation of the coronavirus disease 2019 (COVID-19) from viral pneumonia based on chest CT

Abstract

Purpose In the absence of a virus nucleic acid real-time reverse transcriptase-polymerase chain reaction (RT-PCR) test and experienced radiologists, clinical diagnosis is challenging for viral pneumonia with clinical symptoms and CT signs similar to that of coronavirus disease 2019 (COVID-19). We developed an end-to-end automatic differentiation method based on CT images to identify COVID-19 pneumonia patients in real time. Methods From January 18 to February 23, 2020, we conducted a retrospective study and enrolled 201 patients from two hospitals in China who underwent chest CT and RT-PCR tests, of which 98 patients tested positive for COVID-19 (118 males and 83 females, with an average age of 42 years). Patient CT images from one hospital were divided among training, validation and test datasets with an 80%:10%:10% ratio. An end-to-end representation learning method using a large-scale bi-directional generative adversarial network (BigBiGAN) architecture was designed to extract semantic features from the CT images. The semantic feature matrix was input for linear classifier construction. Patients from the other hospital were used for external validation. Differentiation accuracy was evaluated using a receiver operating characteristic curve. Results Based on the 120-dimensional semantic features extracted by BigBiGAN from each image, the linear classifier results indicated that the area under the curve (AUC) in the training, validation and test datasets were 0.979, 0.968 and 0.972, respectively, with an average sensitivity of 92% and specificity of 91%. The AUC for external validation was 0.850, with a sensitivity of 80% and specificity of 75%. Publicly available architecture and computing resources were used throughout the study to ensure reproducibility. Conclusion This study provides an efficient recognition method for coronavirus disease 2019 pneumonia, using an end-to-end design to implement targeted and effective isolation for the containment of this communicable disease.

On the Role of Artificial Intelligence in Medical Imaging of COVID-19

“…Notably, clinical diagnosis should by no means solely base on algorithmic predictions. DL models combining CT and clinical features start to match [13] or surpass [31] performance of senior radiologists in detecting COVID-19, but the road toward clinical application is long and difficult.…”

“…Some studies demonstrated that radiologists' performance improves upon consultation of AI: Junior radiologists along with AI can perform as well as mid-senior radiologist [30] and radiologists' sensitivity and specificity can improve by nearly 10% through AI [31]. Similarly, in a blind review with six radiologists' decisions with AI support were superior to radiologists alone (sensitivity 88% vs. 79%, specificity 91% vs. 88%), although the AI model alone performed the best (95% and 96%) [32].…”

Fast automated detection of COVID-19 from medical images using convolutional neural networks

“…It was confirmed by the two experienced radiologists from the Youan Hospital that no lesion areas of COVID-19, pneumonia, or influenza were present in the 120 cases. We also collected the CT-data of pneumonia cases from a public data set (images of COVID-19 positive and negative pneumonia patients: ICNP) 56 . The CT-data collected from the Youan hospital contained 95 patients diagnosed with COVID-19, 50 patients diagnosed with influenza and 215 patients diagnosed with pneumonia.…”

Current methods for diagnosis of human coronaviruses: pros and cons

“…In each stage, the lower lobes were more inclined giving raise to CT scores and the highest CT score was obtained in 10 days after the onset of initial symptoms [155]. Song et al [156] developed an automatic differentiation method for the real-time identification of COVIDS-19 infection based on the CT scan. The sensitivity and specificity obtained through this analysis from CT image were 92% and 91%, respectively.…”