• Humans
• Tomography, X-Ray Computed/methods
• Densitometry/methods
• Male
• Female
• Lung/diagnostic imaging
• Middle Aged
• Aged
• Radiographic Image Interpretation, Computer-Assisted/methods
• Neural Networks, Computer
• Lung Diseases, Interstitial/diagnostic imaging
• Pulmonary Emphysema/diagnostic imaging
• Retrospective Studies
• Radiography, Thoracic/methods

• COVID-19
• CT quantitative
• pneumonia
• texture analysis
• thorax
• viral

• Acid-base status
• Acute respiratory syndrome
• COVID-19
• Critically ill patients
• Physicochemical approach

• AI
• COVID-19
• Severity of Illness Index
• accuracy
• deep learning
• digital health intervention
• disease severity
• meta-analysis
• prediction model
• prognosis
• prognostics and health management
• systematic review

• Artificial intelligence
• COVID-19
• portable chest X-ray
• prognosis prediction
• radiomics

• COVID-19
• complexity measure
• d-summable information dimension
• entropy
• lung-damage

• SIP20220415 Instituto Politécnico Nacional
• SIP20220421 Instituto Politécnico Nacional

COVID-19-related acute respiratory distress syndrome (ARDS) is characterized by the presence of signs of microvascular involvement at the CT scan, such as the vascular tree in bud (TIB) and the vascular enlargement pattern (VEP). Recent evidence suggests that TIB could be associated with an increased duration of invasive mechanical ventilation (IMV) and intensive care unit (ICU) stay. The primary objective of this study was to evaluate whether microvascular involvement signs could have a prognostic significance concerning liberation from IMV.

All the COVID-19 patients requiring IMV admitted to 16 Italian ICUs and having a lung CT scan recorded within 3 days from intubation were enrolled in this secondary analysis. Radiologic, clinical and biochemical data were collected.

A total of 139 patients affected by COVID-19 related ARDS were enrolled. After grouping based on TIB or VEP detection, we found no differences in terms of duration of IMV and mortality. Extension of VEP and TIB was significantly correlated with ground-glass opacities (GGOs) and crazy paving pattern extension. A parenchymal extent over 50% of GGO and crazy paving pattern was more frequently observed among non-survivors, while a VEP and TIB extent involving 3 or more lobes was significantly more frequent in non-responders to prone positioning.

The presence of early CT scan signs of microvascular involvement in COVID-19 patients does not appear to be associated with differences in duration of IMV and mortality. However, patients with a high extension of VEP and TIB may have a reduced oxygenation response to prone positioning.

Trial Registration: NCT04411459

The online version contains supplementary material available at 10.1007/s11547-021-01444-7.

• Acute respiratory distress syndrome
• Mechanical ventilation
• Novel coronavirus disease 2019
• Pulmonary perfusion
• Thoracic imaging

• COVID-19
• Chest X-Rays (CXRs)
• classification
• computer-aided diagnosis (CAD)
• convolution neural network (CNN)
• long short-term memory network (LSTM)
• medical imaging

• COVID-19
• COVID-19 Testing
• Computers
• Deep Learning
• Humans
• SARS-CoV-2

• COVID-19
• artificial intelligence
• deep learning

• Acute Disease
• Adult
• COVID-19/blood
• COVID-19/therapy
• Continuous Positive Airway Pressure
• Female
• Humans
• Oxygen/administration & dosage
• Oxygen/blood
• Pregnancy
• Pregnancy Complications, Infectious/blood
• Pregnancy Complications, Infectious/therapy
• Protein C/metabolism
• Respiratory Insufficiency/blood
• Respiratory Insufficiency/therapy
• Retrospective Studies
• SARS-CoV-2/metabolism
• Tertiary Care Centers

Auto-detection of diseases has become a prime issue in medical sciences as population density is fast growing. An intelligent framework for disease detection helps physicians identify illnesses, give reliable and consistent results, and reduce death rates. Coronavirus (Covid-19) has recently been one of the most severe and acute diseases in the world. An automatic detection framework should therefore be introduced as the fastest diagnostic alternative to avoid Covid-19 spread. In this paper, an automatic Covid-19 identification in the CT scan and chest X-ray is obtained with the help of a combined deep learning and multi-level feature extraction methodology. In this method, the multi-level feature extraction approach comprises GIST, Scale Invariant Feature Transform (SIFT), and Convolutional Neural Network (CNN) extract features from CT scans and chest X-rays. The objective of multi-level feature extraction is to reduce the training complexity of CNN network, which significantly assists in accurate and robust Covid-19 identification. Finally, Long Short-Term Memory (LSTM) along the CNN network is used to detect the extracted Covid-19 features. The Kaggle SARS-CoV-2 CT scan dataset and the Italian SIRM Covid-19 CT scan and chest X-ray dataset were employed for testing purposes. Experimental outcomes show that proposed approach obtained 98.94% accuracy with the SARS-CoV-2 CT scan dataset and 83.03% accuracy with the SIRM Covid-19 CT scan and chest X-ray dataset. The proposed approach helps radiologists and practitioners to detect and treat Covid-19 cases effectively over the pandemic.

• Convolutional Neural Network
• Coronavirus
• Covid-19 Severity Classification
• Deep learning
• Long Short-Term Memory
• Multi-level feature extraction

• CT-estimated emphysema
• chronic obstructive pulmonary disease
• computed tomography
• pulmonary emphysema
• quantitative chest CT analysis

• Conselho Nacional de Desenvolvimento Científico e Tecnológico
• Fundação Carlos Chagas Filho de Amparo à Pesquisa do Estado do Rio de Janeiro

• C-reactive protein (CRP)
• Coronavirus disease 2019 (COVID-19)
• computed tomography
• cytokines
• glucocorticoid (GC)

Artificial intelligence (AI) systems have become critical in support of decision-making. This systematic review summarizes all the data currently available on the AI-assisted CT-Scan prediction accuracy for COVID-19. The ISI Web of Science, Cochrane Library, PubMed, Scopus, CINAHL, Science Direct, PROSPERO, and EMBASE were systematically searched. We used the revised Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2) tool to assess all included studies' quality and potential bias. A hierarchical receiver-operating characteristic summary (HSROC) curve and a summary receiver operating characteristic (SROC) curve have been implemented. The area under the curve (AUC) was computed to determine the diagnostic accuracy. Finally, 36 studies (a total of 39,246 image data) were selected for inclusion into the final meta-analysis. The pooled sensitivity for AI was 0.90 (95% CI, 0.90–0.91), specificity was 0.91 (95% CI, 0.90–0.92) and the AUC was 0.96 (95% CI, 0.91–0.98). For deep learning (DL) method, the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.88 (95% CI, 0.87–0.88) and the AUC was 0.96 (95% CI, 0.93–0.97). In case of machine learning (ML), the pooled sensitivity was 0.90 (95% CI, 0.90–0.91), specificity was 0.95 (95% CI, 0.94–0.95) and the AUC was 0.97 (95% CI, 0.96–0.99). AI in COVID-19 patients is useful in identifying symptoms of lung involvement. More prospective real-time trials are required to confirm AI's role for high and quick COVID-19 diagnosis due to the possible selection bias and retrospective existence of currently available studies.

• Artificial intelligence
• COVID-19
• CT-Scan
• Computed tomography
• Coronavirus infections
• Deep learning
• Machine learning
• Respiratory tract infections

• COVID-19
• CT-estimated lung volume
• CT-estimated lung weight
• computed tomography
• deep learning