Next Article in Journal
Influence of Antihistamines on Basophil Activation Test in Food Allergy to Milk and Egg
Previous Article in Journal
Biomarkers for Inner Ear Disorders: Scoping Review on the Role of Biomarkers in Hearing and Balance Disorders
Open AccessArticle

Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19

1
Department of Pneumology, University Hospital of Liège, 4020 Liège, Belgium
2
Research and Development, Oncoradiomics SA, 4000 Liège, Belgium
3
The D-Lab, Department of Precision Medicine, Maastricht University, 6229 Maastricht, The Netherlands
4
Department of Radiology, University Hospital of Liège, 4020 Liège, Belgium
5
Department of Medico-Economic Information, University Hospital of Liège, 4020 Liège, Belgium
6
Department of Computer Applications, University Hospital of Liège, 4020 Liège, Belgium
7
Department of Infectious Diseases, University Hospital of Liège, 4020 Liège, Belgium
8
Department of Nuclear Medicine and Oncological Imaging, University Hospital of Liège, 4020 Liège, Belgium
*
Author to whom correspondence should be addressed.
These authors have contributed equally to this work and should be considered as co-first author.
These authors have contributed equally to this work and should be considered co-senior author.
Diagnostics 2021, 11(1), 41; https://doi.org/10.3390/diagnostics11010041
Received: 12 October 2020 / Revised: 22 December 2020 / Accepted: 23 December 2020 / Published: 30 December 2020
(This article belongs to the Section Machine Learning and Artificial Intelligence in Diagnostics)
The coronavirus disease 2019 (COVID-19) outbreak has reached pandemic status. Drastic measures of social distancing are enforced in society and healthcare systems are being pushed to and beyond their limits. To help in the fight against this threat on human health, a fully automated AI framework was developed to extract radiomics features from volumetric chest computed tomography (CT) exams. The detection model was developed on a dataset of 1381 patients (181 COVID-19 patients plus 1200 non COVID control patients). A second, independent dataset of 197 RT-PCR confirmed COVID-19 patients and 500 control patients was used to assess the performance of the model. Diagnostic performance was assessed by the area under the receiver operating characteristic curve (AUC). The model had an AUC of 0.882 (95% CI: 0.851–0.913) in the independent test dataset (641 patients). The optimal decision threshold, considering the cost of false negatives twice as high as the cost of false positives, resulted in an accuracy of 85.18%, a sensitivity of 69.52%, a specificity of 91.63%, a negative predictive value (NPV) of 94.46% and a positive predictive value (PPV) of 59.44%. Benchmarked against RT-PCR confirmed cases of COVID-19, our AI framework can accurately differentiate COVID-19 from routine clinical conditions in a fully automated fashion. Thus, providing rapid accurate diagnosis in patients suspected of COVID-19 infection, facilitating the timely implementation of isolation procedures and early intervention. View Full-Text
Keywords: artificial intelligence; machine learning; computed tomography; COVID-19; radiomics artificial intelligence; machine learning; computed tomography; COVID-19; radiomics
Show Figures

Figure 1

MDPI and ACS Style

Guiot, J.; Vaidyanathan, A.; Deprez, L.; Zerka, F.; Danthine, D.; Frix, A.-N.; Thys, M.; Henket, M.; Canivet, G.; Mathieu, S.; Eftaxia, E.; Lambin, P.; Tsoutzidis, N.; Miraglio, B.; Walsh, S.; Moutschen, M.; Louis, R.; Meunier, P.; Vos, W.; Leijenaar, R.T.H.; Lovinfosse, P. Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19. Diagnostics 2021, 11, 41. https://doi.org/10.3390/diagnostics11010041

AMA Style

Guiot J, Vaidyanathan A, Deprez L, Zerka F, Danthine D, Frix A-N, Thys M, Henket M, Canivet G, Mathieu S, Eftaxia E, Lambin P, Tsoutzidis N, Miraglio B, Walsh S, Moutschen M, Louis R, Meunier P, Vos W, Leijenaar RTH, Lovinfosse P. Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19. Diagnostics. 2021; 11(1):41. https://doi.org/10.3390/diagnostics11010041

Chicago/Turabian Style

Guiot, Julien; Vaidyanathan, Akshayaa; Deprez, Louis; Zerka, Fadila; Danthine, Denis; Frix, Anne-Noëlle; Thys, Marie; Henket, Monique; Canivet, Gregory; Mathieu, Stephane; Eftaxia, Evanthia; Lambin, Philippe; Tsoutzidis, Nathan; Miraglio, Benjamin; Walsh, Sean; Moutschen, Michel; Louis, Renaud; Meunier, Paul; Vos, Wim; Leijenaar, Ralph T.H.; Lovinfosse, Pierre. 2021. "Development and Validation of an Automated Radiomic CT Signature for Detecting COVID-19" Diagnostics 11, no. 1: 41. https://doi.org/10.3390/diagnostics11010041

Find Other Styles
Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Article Access Map by Country/Region

1
Search more from Scilit
 
Search
Back to TopTop