PT - JOURNAL ARTICLE AU - fadila Zerka AU - Akshayaa Vaidyanathan AU - Julien Guiot AU - Louis Deprez AU - Denis Danthine AU - Grégory Canivet AU - Mathieu Stéphane AU - E Eftaxia AU - Monique Henket AU - M Thys AU - Philippe Lambin AU - Nathan Tsoutzidis AU - Benjamin Miraglio AU - Sean Wlash AU - Paul Meunier AU - Wim Vos AU - Ralph Leijenaar AU - Pierre Lovinfosse TI - Late Breaking Abstract - Development and validation of an automated radiomic CT signature for detecting?COVID-19 AID - 10.1183/13993003.congress-2020.4152 DP - 2020 Sep 07 TA - European Respiratory Journal PG - 4152 VI - 56 IP - suppl 64 4099 - http://erj.ersjournals.com/content/56/suppl_64/4152.short 4100 - http://erj.ersjournals.com/content/56/suppl_64/4152.full SO - Eur Respir J2020 Sep 07; 56 AB - 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 Liège, 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.FootnotesCite this article as: European Respiratory Journal 2020; 56: Suppl. 64, 4152.This abstract was presented at the 2020 ERS International Congress, in session “Respiratory viruses in the "pre COVID-19" era”.This is an ERS International Congress abstract. No full-text version is available. Further material to accompany this abstract may be available at www.ers-education.org (ERS member access only).