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

Chatzitofis, Anargyros; Cancian, Pierandrea; Gkitsas, Vasileios; Carlucci, Alessandro; Stalidis, Panagiotis; Albanis, Georgios; Karakottas, Antonis; Semertzidis, Theodoros; Daras, Petros; Giannitto, Caterina; Casiraghi, Elena; Sposta, Federica Mrakic; Vatteroni, Giulia; Ammirabile, Angela; Lofino, Ludovica; Ragucci, Pasquala; Laino, Maria Elena; Voza, Antonio; Desai, Antonio; Cecconi, Maurizio; Balzarini, Luca; Chiti, Arturo; Zarpalas, Dimitrios; Savevski, Victor

doi:10.3390/ijerph18062842

Cited by 9 publications

(13 citation statements)

References 52 publications

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“…It resulted in an excellent performance for the prediction of disease severity (AUC of 0.892) that is, in turn, positively correlated with area and density of lung lesions. Moreover, the clinical significance of the model relied on the possibility to identify mild disease in early stages that could progress to a more severe form, characterized by a lower survival probability [97].…”

Section: Ai In the Stratification And Definition Of Severity And Complications Of Covid-19 Pneumonia At Chest Ctmentioning

confidence: 99%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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Diagnostic imaging is regarded as fundamental in the clinical work-up of patients with a suspected or confirmed COVID-19 infection. Recent progress has been made in diagnostic imaging with the integration of artificial intelligence (AI) and machine learning (ML) algorisms leading to an increase in the accuracy of exam interpretation and to the extraction of prognostic information useful in the decision-making process. Considering the ever expanding imaging data generated amid this pandemic, COVID-19 has catalyzed the rapid expansion in the application of AI to combat disease. In this context, many recent studies have explored the role of AI in each of the presumed applications for COVID-19 infection chest imaging, suggesting that implementing AI applications for chest imaging can be a great asset for fast and precise disease screening, identification and characterization. However, various biases should be overcome in the development of further ML-based algorithms to give them sufficient robustness and reproducibility for their integration into clinical practice. As a result, in this literature review, we will focus on the application of AI in chest imaging, in particular, deep learning, radiomics and advanced imaging as quantitative CT.

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Section: Ai In the Stratification And Definition Of Severity And Complications Of Covid-19 Pneumonia At Chest Ctmentioning

confidence: 99%

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

et al. 2021

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“…Their two-stage data-driven algorithm was able to stratify patients into three groups (moderate, severe, and extreme) according to the possibility of being discharged, hospitalized, or admitted to ICU, respectively. Using the COVID-19_CHDSET Dataset (annotated CT dataset of COVID-19 patients from Milan) as the training set, the developed algorithm with DenseNet201-VoI as backbone model yielded an AUC of 0.97, 0.92, and 1.00 for the three classes, respectively, and accuracy of 88.88%, specificity of 94.73%, and sensitivity of 89.77% [ 70 ].…”

Section: Chest Ct and Artificial Intelligence In Covid-19 Patients For The Prediction Of Icu Admissionmentioning

confidence: 99%

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

et al. 2022

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Infection with SARS-CoV-2 has dominated discussion and caused global healthcare and economic crisis over the past 18 months. Coronavirus disease 19 (COVID-19) causes mild-to-moderate symptoms in most individuals. However, rapid deterioration to severe disease with or without acute respiratory distress syndrome (ARDS) can occur within 1–2 weeks from the onset of symptoms in a proportion of patients. Early identification by risk stratifying such patients who are at risk of severe complications of COVID-19 is of great clinical importance. Computed tomography (CT) is widely available and offers the potential for fast triage, robust, rapid, and minimally invasive diagnosis: Ground glass opacities (GGO), crazy-paving pattern (GGO with superimposed septal thickening), and consolidation are the most common chest CT findings in COVID pneumonia. There is growing interest in the prognostic value of baseline chest CT since an early risk stratification of patients with COVID-19 would allow for better resource allocation and could help improve outcomes. Recent studies have demonstrated the utility of baseline chest CT to predict intensive care unit (ICU) admission in patients with COVID-19. Furthermore, developments and progress integrating artificial intelligence (AI) with computer-aided design (CAD) software for diagnostic imaging allow for objective, unbiased, and rapid assessment of CT images.

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“…In [14], a data-driven approach built on top of volume-of-interest aware deep neural networks for automatic COVID-19 patient risk assessment based on lung infection quantization through segmentation and CT classification was proposed. The high and varying dimensionality of the CT input was detected and analyzed with reference to a sub-volume of the CT, named the Volume-of-Interest (VoI).…”

Section: Related Workmentioning

confidence: 99%

Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

Manzo

Pellino²

2021

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COVID-19 has been a great challenge for humanity since the year 2020. The whole world has made a huge effort to find an effective vaccine in order to save those not yet infected. The alternative solution is early diagnosis, carried out through real-time polymerase chain reaction (RT-PCR) tests or thorax Computer Tomography (CT) scan images. Deep learning algorithms, specifically convolutional neural networks, represent a methodology for image analysis. They optimize the classification design task, which is essential for an automatic approach with different types of images, including medical. In this paper, we adopt a pretrained deep convolutional neural network architecture in order to diagnose COVID-19 disease from CT images. Our idea is inspired by what the whole of humanity is achieving, as the set of multiple contributions is better than any single one for the fight against the pandemic. First, we adapt, and subsequently retrain for our assumption, some neural architectures that have been adopted in other application domains. Secondly, we combine the knowledge extracted from images by the neural architectures in an ensemble classification context. Our experimental phase is performed on a CT image dataset, and the results obtained show the effectiveness of the proposed approach with respect to the state-of-the-art competitors.

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

Cited by 9 publications

References 52 publications

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

The Applications of Artificial Intelligence in Chest Imaging of COVID-19 Patients: A Literature Review

Prognostic findings for ICU admission in patients with COVID-19 pneumonia: baseline and follow-up chest CT and the added value of artificial intelligence

Fighting Together against the Pandemic: Learning Multiple Models on Tomography Images for COVID-19 Diagnosis

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