CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

Karthik, R.; Menaka, R.; Hariharan, M.; Won, Deahan

doi:10.1016/j.asoc.2022.108765

Cited by 17 publications

(10 citation statements)

References 51 publications

(72 reference statements)

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“…In [27] , proposes a Weak Variational Autoencoder for Localisation and Enhancement framework, in order to solve the problem of effective anomaly localisation without pixel-level annotations, with a new gradient-based technique for variational autoencoders in localisation of COVID-19 lung infection regions, and use of post-hoc attention maps to generate pseudo segmentation datasets for images. In [28] propose a novel attention framework to estimate weakly annotated CT COVID-19 dataset, a non-locality approach that correlates ground-glass opacities and consolidations features across different parts and spatial scales of the 3D Scan. In [29] , the main contribution is to build a deep learning model using normal CT images and then perform focal zone labeling on unlabeled images using a focal zone feature recognition approach to demonstrate the validity of his proposed integration of COVID-19 positive diagnosis and lesion analysis into a unified framework, and that the approach can be extended to other needle-detection applications for chest diseases.…”

Section: Relate Workmentioning

confidence: 99%

“…For weakly supervised learning, studying local autocorrelation is a better entry point to break through the lack of label information. Research on weakly supervised learning of medical images has been reported rarely [9] , [10] , [28] , and it is in the initial stage of development. Therefore, the need to propose an effective and simple segmentation network model appears to be of great research value.…”

Section: Research Gapsmentioning

confidence: 99%

“…Research on weakly supervised learning of medical images has been reported rarely [9] , [10] , [28] , and it is in the initial stage of development. Therefore, the need to propose an effective and simple segmentation network model appears to be of great research value.…”

Section: Research Gapsmentioning

confidence: 99%

See 2 more Smart Citations

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Sun

Feng²,

Liu³

et al. 2023

Biomedical Signal Processing and Control

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Section: Relate Workmentioning

confidence: 99%

Section: Research Gapsmentioning

confidence: 99%

See 1 more Smart Citation

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Sun

Feng²,

Liu³

et al. 2023

Biomedical Signal Processing and Control

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“…Their identification and the volumetric quantification may allow an easier classification in terms of gravity, extent and progression of the disease. Moreover, this may provide a high-impact tool to enhance awareness of the severity of COVID-19 pneumonia [ 75 , 76 , 77 , 78 , 79 , 80 , 81 , 82 , 83 , 84 , 85 , 86 , 87 , 88 , 89 , 90 ].…”

Section: Introductionmentioning

confidence: 99%

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

Granata

Fusco

Villanacci

et al. 2022

JPM

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Purpose: To analyze the vaccine effect by comparing five groups: unvaccinated patients with Alpha variant, unvaccinated patients with Delta variant, vaccinated patients with Delta variant, unvaccinated patients with Omicron variant, and vaccinated patients with Omicron variant, assessing the “gravity” of COVID-19 pulmonary involvement, based on CT findings in critically ill patients admitted to Intensive Care Unit (ICU). Methods: Patients were selected by ICU database considering the period from December 2021 to 23 March 2022, according to the following inclusion criteria: patients with proven Omicron variant COVID-19 infection with known COVID-19 vaccination with at least two doses and with chest Computed Tomography (CT) study during ICU hospitalization. Wee also evaluated the ICU database considering the period from March 2020 to December 2021, to select unvaccinated consecutive patients with Alpha variant, subjected to CT study, consecutive unvaccinated and vaccinated patients with Delta variant, subjected to CT study, and, consecutive unvaccinated patients with Omicron variant, subjected to CT study. CT images were evaluated qualitatively using a severity score scale of 5 levels (none involvement, mild: ≤25% of involvement, moderate: 26–50% of involvement, severe: 51–75% of involvement, and critical involvement: 76–100%) and quantitatively, using the Philips IntelliSpace Portal clinical application CT COPD computer tool. For each patient the lung volumetry was performed identifying the percentage value of aerated residual lung volume. Non-parametric tests for continuous and categorical variables were performed to assess statistically significant differences among groups. Results: The patient study group was composed of 13 vaccinated patients affected by the Omicron variant (Omicron V). As control groups we identified: 20 unvaccinated patients with Alpha variant (Alpha NV); 20 unvaccinated patients with Delta variant (Delta NV); 18 vaccinated patients with Delta variant (Delta V); and 20 unvaccinated patients affected by the Omicron variant (Omicron NV). No differences between the groups under examination were found (p value > 0.05 at Chi square test) in terms of risk factors (age, cardiovascular diseases, diabetes, immunosuppression, chronic kidney, cardiac, pulmonary, neurologic, and liver disease, etc.). A different median value of aerated residual lung volume was observed in the Delta variant groups: median value of aerated residual lung volume was 46.70% in unvaccinated patients compared to 67.10% in vaccinated patients. In addition, in patients with Delta variant every other extracted volume by automatic tool showed a statistically significant difference between vaccinated and unvaccinated group. Statistically significant differences were observed for each extracted volume by automatic tool between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant of COVID-19. Good statistically significant correlations among volumes extracted by automatic tool for each lung lobe and overall radiological severity score were obtained (ICC range 0.71–0.86). GGO was the main sign of COVID-19 lesions on CT images found in 87 of the 91 (95.6%) patients. No statistically significant differences were observed in CT findings (ground glass opacities (GGO), consolidation or crazy paving sign) among patient groups. Conclusion: In our study, we showed that in critically ill patients no difference were observed in terms of severity of disease or exitus, between unvaccinated and vaccinated patients. The only statistically significant differences were observed, with regard to the severity of COVID-19 pulmonary parenchymal involvement, between unvaccinated patients affected by Alpha variant and vaccinated patients affected by Delta variant, and between unvaccinated patients with Delta variant and vaccinated patients with Delta variant.

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“…Additional prognostic modeling capabilities were tested through risk stratification ability through classification into a high-risk group with a mean survival time of 13.5 days or a low-risk group with a mean survival time of 23 days. Authors in [ 148 ] utilized a CNN model to assess COVID-19 severity in patients. The dataset that was used was MosMed which contained 1110 CT scans.…”

Section: Covid-19 Prognostic and Longitudinal Modelsmentioning

confidence: 99%

A Comprehensive Review of Machine Learning Used to Combat COVID-19

et al. 2022

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Coronavirus disease (COVID-19) has had a significant impact on global health since the start of the pandemic in 2019. As of June 2022, over 539 million cases have been confirmed worldwide with over 6.3 million deaths as a result. Artificial Intelligence (AI) solutions such as machine learning and deep learning have played a major part in this pandemic for the diagnosis and treatment of COVID-19. In this research, we review these modern tools deployed to solve a variety of complex problems. We explore research that focused on analyzing medical images using AI models for identification, classification, and tissue segmentation of the disease. We also explore prognostic models that were developed to predict health outcomes and optimize the allocation of scarce medical resources. Longitudinal studies were conducted to better understand COVID-19 and its effects on patients over a period of time. This comprehensive review of the different AI methods and modeling efforts will shed light on the role that AI has played and what path it intends to take in the fight against COVID-19.

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CT-based severity assessment for COVID-19 using weakly supervised non-local CNN

Cited by 17 publications

References 51 publications

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Weakly supervised segmentation of COVID-19 infection with local lesion coherence on CT images

Imaging Severity COVID-19 Assessment in Vaccinated and Unvaccinated Patients: Comparison of the Different Variants in a High Volume Italian Reference Center

A Comprehensive Review of Machine Learning Used to Combat COVID-19

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