Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels

Wu, Dufan; Gong, Kuang; Arru, Chiara; Homayounieh, Fatemeh; Bizzo, Bernardo C.; Buch, Varun; Hui, Rutai; Kim, Kyungsang; Neumark, Nir; Xu, Pengcheng; Liu, Zhiyuan; Fang, Wei; Xie, Nuobei; Tak, Won Young; Park, Soo Young; Lee, Yu Rim; Kang, Min Kyu; Park, Jung Gil; Carriero, Alessandro; Saba, Luca; Masjedi, Mahsa; Talari, Hamid Reza; Babaei, Rosa; Mobin, Hadi Karimi; Ebrahimian, Shadi; Dayan, Ittai; Kalra, Mannudeep K.; Li, Quanzheng

doi:10.1109/jbhi.2020.3030224

Cited by 36 publications

(22 citation statements)

References 37 publications

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“…With the state-of-the-art data analysis strategy, artificial intelligence (AI) technologies have achieved remarkable success in medical imaging analysis. Numerous studies have shown great potential in automated quantification of lung abnormalities and severity prediction applying AI-based technologies (20)(21)(22).…”

Section: Introductionmentioning

confidence: 99%

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

et al. 2021

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Objective: To assess CT features of COVID-19 patients with different smoking status using quantitative and semi-quantitative technologies and to investigate changes of CT features in different disease states between the two groups.Methods: 30 COVID-19 patients with current smoking status (29 men, 1 woman) admitted in our database were enrolled as smoking group and 56 COVID-19 patients without smoking history (24 men, 32 women) admitted during the same period were enrolled as a control group. Twenty-seven smoking cases and 55 control cases reached recovery standard and were discharged. Initial and follow-up CT during hospitalization and follow-up CT after discharge were acquired. Thirty quantitative features, including the ratio of infection volume and visual-assessed interstitial changes score including total score, score of ground glass opacity, consolidation, septal thickening, reticulation and honeycombing sign, were analyzed.Results: Initial CT images of the smoking group showed higher scores of septal thickening [4.5 (0–5) vs. 0 (0–4), p = 0.001] and reticulation [0 (0–5.25) vs 0 (0–0), p = 0.001] as well as higher total score [7 (5–12.25) vs. 6 (5–7), p = 0.008] with statistical significance than in the control group. The score of reticulation was higher in the smoking group than in the control group when discharged [0.89 (0–0) vs. 0.09 (0–0), p = 0.02]. The score of septal thickening tended to be higher in the smoking group than the control group [4 (0–4) vs. 0 (0–4), p = 0.007] after being discharged. Quantitative CT features including infection ratio of whole lung and left lung as well as infection ratio within HU (−750, −300) and within HU (−300, 49) were higher in the control group of initial CT with statistical differences. The infection ratio of whole lung and left lung, infection ratio within HU (−750), and within HU (−750, −300) were higher in the control group with statistical differences when discharged. This trend turned adverse after discharge and the values of quantitative features were generally higher in the smoking group than in the control group without statistical differences.Conclusions: Patients with a history of smoking presented more severe interstitial manifestations and more residual lesion after being discharged. More support should be given for COVID-19 patients with a smoking history during hospitalization and after discharge.

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Section: Introductionmentioning

confidence: 99%

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

et al. 2021

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“…To overcome the pitfalls of noisy labels, Wang et al [ 30 ] have developed a noise-robust segmentation network through the usage of a novel Dice loss and an adaptive self-ensembling training framework. Wu et al [ 31 ] have developed a weakly supervised network based on hybrid labels to segment both opacity and consolidation regions from COVID-19 CT datasets. Amyar et al [ 16 ] used the multi-task loss to improve the segmentation of infection regions in COVID-19 CT images.…”

Section: Discussionmentioning

confidence: 99%

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records

Gong

Arru

et al. 2021

European Journal of Radiology

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“…The extent of GGO and consolidation can evaluate the disease severity of COVID-19 (50). As viruses spread via the respiratory mucosa and also infect other cells, they induce a cytokine storm and a series of immune responses that cause changes in peripheral blood and immune cells (51).…”

Section: Discussionmentioning

confidence: 99%

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

Pang¹,

Liu

et al. 2021

Front. Med.

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Objective: Early identification of coronavirus disease 2019 (COVID-19) patients with worse outcomes may benefit clinical management of patients. We aimed to quantify pneumonia findings on CT at admission to predict progression to critical illness in COVID-19 patients.Methods: This retrospective study included laboratory-confirmed adult patients with COVID-19. All patients underwent a thin-section chest computed tomography (CT) scans showing evidence of pneumonia. CT images with severe moving artifacts were excluded from analysis. Patients' clinical and laboratory data were collected from medical records. Three quantitative CT features of pneumonia lesions were automatically calculated using a care.ai Intelligent Multi-disciplinary Imaging Diagnosis Platform Intelligent Evaluation System of Chest CT for COVID-19, denoting the percentage of pneumonia volume (PPV), ground-glass opacity volume (PGV), and consolidation volume (PCV). According to Chinese COVID-19 guidelines (trial version 7), patients were divided into noncritical and critical groups. Critical illness was defined as a composite of admission to the intensive care unit, respiratory failure requiring mechanical ventilation, shock, or death. The performance of PPV, PGV, and PCV in discrimination of critical illness was assessed. The correlations between PPV and laboratory variables were assessed by Pearson correlation analysis.Results: A total of 140 patients were included, with mean age of 58.6 years, and 85 (60.7%) were male. Thirty-two (22.9%) patients were critical. Using a cutoff value of 22.6%, the PPV had the highest performance in predicting critical illness, with an area under the curve of 0.868, sensitivity of 81.3%, and specificity of 80.6%. The PPV had moderately positive correlation with neutrophil (%) (r = 0.535, p < 0.001), erythrocyte sedimentation rate (r = 0.567, p < 0.001), d-Dimer (r = 0.444, p < 0.001), high-sensitivity C-reactive protein (r = 0.495, p < 0.001), aspartate aminotransferase (r = 0.410, p < 0.001), lactate dehydrogenase (r = 0.644, p < 0.001), and urea nitrogen (r = 0.439, p < 0.001), whereas the PPV had moderately negative correlation with lymphocyte (%) (r = −0.535, p < 0.001).Conclusions: Pneumonia volume quantified on initial CT can non-invasively predict the progression to critical illness in advance, which serve as a prognostic marker of COVID-19.

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Severity and Consolidation Quantification of COVID-19 From CT Images Using Deep Learning Based on Hybrid Weak Labels

Cited by 36 publications

References 37 publications

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

The Differences and Changes of Semi-Quantitative and Quantitative CT Features of Coronavirus Disease 2019 Pneumonia in Patients With or Without Smoking History

A multi-center study of COVID-19 patient prognosis using deep learning-based CT image analysis and electronic health records

CT Quantification of COVID-19 Pneumonia at Admission Can Predict Progression to Critical Illness: A Retrospective Multicenter Cohort Study

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