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Article

Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment

1
Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
2
Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
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Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
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Dipartimento di Informatica/Computer Science Department “Giovanni degli Antoni”, Università degli Studi di Milano, Via Celoria 18, 20133 Milan, Italy
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Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
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Emergency Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
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Intensive Care Unit, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
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Humanitas Clinical and Research Center—IRCCS, Via Manzoni 56, 20089 Rozzano, Italy
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Academic Editors: Santoso Wibowo and A. B. M. Shawkat Ali
Int. J. Environ. Res. Public Health 2021, 18(6), 2842; https://doi.org/10.3390/ijerph18062842
Received: 22 January 2021 / Revised: 19 February 2021 / Accepted: 3 March 2021 / Published: 11 March 2021
(This article belongs to the Special Issue Deep Learning: AI Steps Up in Battle against COVID-19)
Since December 2019, the world has been devastated by the Coronavirus Disease 2019 (COVID-19) pandemic. Emergency Departments have been experiencing situations of urgency where clinical experts, without long experience and mature means in the fight against COVID-19, have to rapidly decide the most proper patient treatment. In this context, we introduce an artificially intelligent tool for effective and efficient Computed Tomography (CT)-based risk assessment to improve treatment and patient care. In this paper, we introduce a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment (discharged, hospitalized, intensive care unit) based on lung infection quantization through segmentation and, subsequently, CT classification. We tackle the high and varying dimensionality of the CT input by detecting and analyzing only a sub-volume of the CT, the Volume-of-Interest (VoI). Differently from recent strategies that consider infected CT slices without requiring any spatial coherency between them, or use the whole lung volume by applying abrupt and lossy volume down-sampling, we assess only the “most infected volume” composed of slices at its original spatial resolution. To achieve the above, we create, present and publish a new labeled and annotated CT dataset with 626 CT samples from COVID-19 patients. The comparison against such strategies proves the effectiveness of our VoI-based approach. We achieve remarkable performance on patient risk assessment evaluated on balanced data by reaching 88.88%, 89.77%, 94.73% and 88.88% accuracy, sensitivity, specificity and F1-score, respectively. View Full-Text
Keywords: COVID-19; artificial intelligence; deep learning; CT-based diagnosis; patient risk assessment; infection quantification; patient stratification COVID-19; artificial intelligence; deep learning; CT-based diagnosis; patient risk assessment; infection quantification; patient stratification
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MDPI and ACS Style

Chatzitofis, A.; Cancian, P.; Gkitsas, V.; Carlucci, A.; Stalidis, P.; Albanis, G.; Karakottas, A.; Semertzidis, T.; Daras, P.; Giannitto, C.; Casiraghi, E.; Sposta, F.M.; Vatteroni, G.; Ammirabile, A.; Lofino, L.; Ragucci, P.; Laino, M.E.; Voza, A.; Desai, A.; Cecconi, M.; Balzarini, L.; Chiti, A.; Zarpalas, D.; Savevski, V. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. Int. J. Environ. Res. Public Health 2021, 18, 2842. https://doi.org/10.3390/ijerph18062842

AMA Style

Chatzitofis A, Cancian P, Gkitsas V, Carlucci A, Stalidis P, Albanis G, Karakottas A, Semertzidis T, Daras P, Giannitto C, Casiraghi E, Sposta FM, Vatteroni G, Ammirabile A, Lofino L, Ragucci P, Laino ME, Voza A, Desai A, Cecconi M, Balzarini L, Chiti A, Zarpalas D, Savevski V. Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment. International Journal of Environmental Research and Public Health. 2021; 18(6):2842. https://doi.org/10.3390/ijerph18062842

Chicago/Turabian Style

Chatzitofis, Anargyros, Pierandrea Cancian, Vasileios Gkitsas, Alessandro Carlucci, Panagiotis Stalidis, Georgios Albanis, Antonis Karakottas, Theodoros Semertzidis, Petros Daras, Caterina Giannitto, Elena Casiraghi, Federica M. Sposta, Giulia Vatteroni, Angela Ammirabile, Ludovica Lofino, Pasquala Ragucci, Maria E. Laino, Antonio Voza, Antonio Desai, Maurizio Cecconi, Luca Balzarini, Arturo Chiti, Dimitrios Zarpalas, and Victor Savevski. 2021. "Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment" International Journal of Environmental Research and Public Health 18, no. 6: 2842. https://doi.org/10.3390/ijerph18062842

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