Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting?COVID-19

The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status and pushed healthcare systems beyond the limits. We aim to develop a fully automatic framework to detect COVID-19 by applying artificial intelligence (AI). A fully automated AI framework was developed to extract radiomics features from chest CT scans to detect COVID-19 patients. We curated and analysed the data from a total of 1381 patients. A cohort of 181 RT-PCR confirmed COVID-19 patients and 1200 control patients was included for model development. An independent dataset of 697 patients was used to validate the model. The datasets were collected from CHU Liege, Belgium. Model performance was assessed by the area under the receiver operating characteristic curve (AUC). Assuming 15% disease prevalence, a comprehensive analysis of classification performance in terms of accuracy, sensitivity, specificity, negative predictive value (NPV) and positive predictive value (PPV) was performed for all possible decision thresholds. The final curated dataset used for model development and testing consisted of chest CT scans of 1224 patients and 641 patients, respectively. The model had an AUC of 0.882 (95% CI: 0.851-0.913) in the independent test dataset. Assuming the cost of false negatives is twice as high as the cost of false positives, the optimal decision threshold resulted in an accuracy of 85.18%, a sensitivity of 69.52, a specificity of 91.63%, an NPV of 94.46% and a PPV of 59.44%. Our AI framework can accurately detect COVID-19. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the implementation of isolation procedures and early intervention.