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Three board-certified radiologists from three countries assessed randomly selected subcohorts from each population and provided CO-RADS\u2013based annotations. CT radiomic features were extracted from the selected subcohorts after preprocessing steps like lung lobe segmentation and automatic noise reduction. We compared three machine learning models, logistic regression (LR), multilayer perceptron (MLP), and random forest (RF) for the automated CO-RADS classification. Model evaluation was carried out in two scenarios, first, training on a mixed multi-demographic subcohort and testing on an independent hold-out dataset. In the second scenario, training was done on a single demography and externally validated on the other demography.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Results<\/jats:title>\n                <jats:p>The overall inter-observer agreement for the CO-RADS scoring between the radiologists was substantial (<jats:italic>k<\/jats:italic> = 0.80). Irrespective of the type of validation test scenario, suspected COVID-19 CT scans were identified with an accuracy of 84%. SHapley Additive exPlanations (SHAP) interpretation showed that the \u201cwavelet_(LH)_GLCM_Imc1\u201d feature had a positive impact on COVID prediction both with and without noise reduction. The application of noise reduction improved the overall performance between the classifiers for all types.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Conclusion<\/jats:title>\n                <jats:p>Using an automated model based on the COVID-19 Reporting and Data System (CO-RADS), we achieved clinically acceptable performance in a multi-demographic setting. This approach can serve as a standardized tool for automated COVID-19 assessment.<\/jats:p>\n              <\/jats:sec><jats:sec>\n                <jats:title>Keypoints<\/jats:title>\n                <jats:p><jats:italic>\u2022 Automatic CO-RADS scoring of large-scale multi-demographic chest CTs with mean AUC of 0.93 \u00b1 0.04.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Validation procedure resembles TRIPOD 2b and 3 categories, enhancing the quality of experimental design to test the cross-dataset domain shift between institutions aiding clinical integration.<\/jats:italic><\/jats:p>\n                <jats:p><jats:italic>\u2022 Identification of COVID-19 pneumonia in the presence of community-acquired pneumonia and other comorbidities with an AUC of 0.92.<\/jats:italic><\/jats:p>\n              <\/jats:sec>","DOI":"10.1007\/s00330-022-08730-6","type":"journal-article","created":{"date-parts":[[2022,4,1]],"date-time":"2022-04-01T15:38:13Z","timestamp":1648827493000},"page":"6384-6396","update-policy":"https:\/\/doi.org\/10.1007\/springer_crossmark_policy","source":"Crossref","is-referenced-by-count":5,"title":["Facilitating standardized COVID-19 suspicion prediction based on computed tomography radiomics in a multi-demographic setting"],"prefix":"10.1007","volume":"32","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-9288-9537","authenticated-orcid":false,"given":"Yeshaswini","family":"Nagaraj","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gonda","family":"de Jonge","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Anna","family":"Andreychenko","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Gabriele","family":"Presti","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthias A.","family":"Fink","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Nikolay","family":"Pavlov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Carlo C.","family":"Quattrocchi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sergey","family":"Morozov","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Raymond","family":"Veldhuis","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Matthijs","family":"Oudkerk","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Peter M. 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