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

Anargyros Chatzitofis, Pierandrea Cancian, Vasileios Gkitsas, Alessandro Carlucci, Panagiotis Stalidis, Georgios Albanis, Antonis Karakottas, Theodoros Semertzidis, Petros Daras, Caterina Giannitto, Elena Casiraghi, Federica Mrakic Sposta, Giulia Vatteroni, Angela Ammirabile, Ludovica Lofino, Pasquala Ragucci, Maria Elena Laino, Antonio Voza, Antonio Desai, Maurizio Cecconi, Luca Balzarini, Arturo Chiti, Dimitrios Zarpalas and Victor Savevski
Additional contact information
Anargyros Chatzitofis: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Pierandrea Cancian: Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Vasileios Gkitsas: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Alessandro Carlucci: Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Panagiotis Stalidis: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Georgios Albanis: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Antonis Karakottas: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Theodoros Semertzidis: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Petros Daras: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Caterina Giannitto: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Elena Casiraghi: Dipartimento di Informatica/Computer Science Department “Giovanni degli Antoni”, Università degli Studi di Milano, Via Celoria 18, 20133 Milan, Italy
Federica Mrakic Sposta: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Giulia Vatteroni: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Angela Ammirabile: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Ludovica Lofino: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Pasquala Ragucci: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Maria Elena Laino: Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Antonio Voza: Emergency Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Antonio Desai: Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
Maurizio Cecconi: Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
Luca Balzarini: Radiology Department, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy
Arturo Chiti: Department of Biomedical Sciences, Humanitas University, Via Rita Levi Montalcini 4, 20090 Pieve Emanuele, Italy
Dimitrios Zarpalas: Centre for Research and Technology Hellas, Information Technologies Institute, 6th km Charilaou—Thermi, P.O. Box 60361, 57001 Thessaloniki, Greece
Victor Savevski: Humanitas AI Center, Humanitas Research Hospital, Via Alessandro Manzoni 56, 20089 Rozzano, Italy

IJERPH, 2021, vol. 18, issue 6, 1-18

Abstract: 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.

Keywords: COVID-19; artificial intelligence; deep learning; CT-based diagnosis; patient risk assessment; infection quantification; patient stratification (search for similar items in EconPapers)
JEL-codes: I I1 I3 Q Q5 (search for similar items in EconPapers)
Date: 2021
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