Volume-of-Interest Aware Deep Neural Networks for Rapid Chest CT-Based COVID-19 Patient Risk Assessment
| Autor: | Elena Casiraghi, Ludovica Lofino, Georgios Albanis, Federica Mrakic Sposta, Angela Ammirabile, Theodoros Semertzidis, Antonio Voza, Pierandrea Cancian, Giulia Vatteroni, Luca Balzarini, Vasileios Gkitsas, Pasquala Ragucci, Dimitrios Zarpalas, Anargyros Chatzitofis, Panagiotis Stalidis, Maria Elena Laino, Victor Savevski, Caterina Giannitto, Antonis Karakottas, Petros Daras, Maurizio Cecconi, Arturo Chiti, Alessandro Carlucci, Antonio Desai |
|---|---|
| Jazyk: | angličtina |
| Rok vydání: | 2021 |
| Předmět: |
medicine.medical_specialty
Coronavirus disease 2019 (COVID-19) Computer science Health Toxicology and Mutagenesis lcsh:Medicine Context (language use) Risk Assessment Article 030218 nuclear medicine & medical imaging law.invention 03 medical and health sciences CT-based diagnosis 0302 clinical medicine law medicine Humans Segmentation Medical physics Quantization (image processing) infection quantification business.industry SARS-CoV-2 Deep learning lcsh:R Public Health Environmental and Occupational Health COVID-19 deep learning patient stratification artificial intelligence Intensive care unit patient risk assessment 3. Good health Artificial intelligence Neural Networks Computer Risk assessment business Tomography X-Ray Computed 030217 neurology & neurosurgery Volume (compression) |
| Zdroj: | International Journal of Environmental Research and Public Health, Vol 18, Iss 2842, p 2842 (2021) International Journal of Environmental Research and Public Health Volume 18 Issue 6 |
| ISSN: | 1661-7827 1660-4601 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
|