SS-TBN: A Semi-Supervised Tri-Branch Network for COVID-19 Screening and Lesion Segmentation.

Insufficient annotated data and minor lung lesions pose big challenges for computed tomography (CT)-aided automatic COVID-19 diagnosis at an early outbreak stage. To address this issue, we propose a Semi-Supervised Tri-Branch Network (SS-TBN). First, we develop a joint TBN model for dual-task application scenarios of image segmentation and classification such as CT-based COVID-19 diagnosis, in which pixel-level lesion segmentation and slice-level infection classification branches are simultaneously trained via lesion attention, and individual-level diagnosis branch aggregates slice-level outputs for COVID-19 screening. Second, we propose a novel hybrid semi-supervised learning method to make full use of unlabeled data, combining a new double-threshold pseudo labeling method specifically designed to the joint model and a new inter-slice consistency regularization method specifically tailored to CT images. Besides two publicly available external datasets, we collect internal and our own external datasets including 210,395 images (1,420 cases versus 498 controls) from ten hospitals. Experimental results show that the proposed method achieves state-of-the-art performance in COVID-19 classification with limited annotated data even if lesions are subtle, and that segmentation results promote interpretability for diagnosis, suggesting the potential of the SS-TBN in early screening in insufficient labeled data situations at the early stage of a pandemic outbreak like COVID-19.

A cross-sectional study on the response abilities of clinical and preventive medical students in public health emergency.

Inconsistent training programs for public health emergency (PHE) have been criticized as a contributing factor in PHE's managerial weak points. In response, to analyze the relevant discrepancies among the medical students in the class of 2021 from Xiangya School of Medicine of Central South University, the present study conducted an online questionnaire survey using convenience sampling. The questionnaire comprised four sections, including the basic information, the subjective cognition in PHE, the rescue knowledge and capabilities of PHE, and the mastery of PHE regulations and psychological intervention abilities. To compare the abovementioned aspects, related data were collected from 235 medical students divided into two groups, namely, clinical medical students (Group A) and preventive medical students (Group B). We found a more positive attitude in PHE (P = 0.014) and a better grasp of the PHE classification (P = 0.027) and the reporting system in group B compared with group A. In addition, even if group B showed the same response capability in communicable diseases as group A, the former had less access to clinical practice, resulting in poorer performance in the noncommunicable diseases during a fire, flood, and traffic accidents (P = 0.002, P = 0.018, P = 0.002). The different emphasis of each training program contributed to the uneven distribution of abilities and cognition. Meanwhile, the lack of an integrated PHE curriculum led to unsystematic expertise. Hence, to optimize the PHE management system, equal attention should be paid to medical students with diverse majors along with a complete integrated PHE curriculum.

Machine learning based on clinical characteristics and chest CT quantitative measurements for prediction of adverse clinical outcomes in hospitalized patients with COVID-19.

OBJECTIVES: To develop and validate a machine learning model for the prediction of adverse outcomes in hospitalized patients with COVID-19. METHODS: We included 424 patients with non-severe COVID-19 on admission from January 17, 2020, to February 17, 2020, in the primary cohort of this retrospective multicenter study. The extent of lung involvement was quantified on chest CT images by a deep learning-based framework. The composite endpoint was the occurrence of severe or critical COVID-19 or death during hospitalization. The optimal machine learning classifier and feature subset were selected for model construction. The performance was further tested in an external validation cohort consisting of 98 patients. RESULTS: There was no significant difference in the prevalence of adverse outcomes (8.7% vs. 8.2%, p = 0.858) between the primary and validation cohorts. The machine learning method extreme gradient boosting (XGBoost) and optimal feature subset including lactic dehydrogenase (LDH), presence of comorbidity, CT lesion ratio (lesion%), and hypersensitive cardiac troponin I (hs-cTnI) were selected for model construction. The XGBoost classifier based on the optimal feature subset performed well for the prediction of developing adverse outcomes in the primary and validation cohorts, with AUCs of 0.959 (95% confidence interval [CI]: 0.936-0.976) and 0.953 (95% CI: 0.891-0.986), respectively. Furthermore, the XGBoost classifier also showed clinical usefulness. CONCLUSIONS: We presented a machine learning model that could be effectively used as a predictor of adverse outcomes in hospitalized patients with COVID-19, opening up the possibility for patient stratification and treatment allocation. KEY POINTS: • Developing an individually prognostic model for COVID-19 has the potential to allow efficient allocation of medical resources. • We proposed a deep learning-based framework for accurate lung involvement quantification on chest CT images. • Machine learning based on clinical and CT variables can facilitate the prediction of adverse outcomes of COVID-19.

Association of Paraspinal Muscle Measurements on Chest Computed Tomography With Clinical Outcomes in Patients With Severe Coronavirus Disease 2019.

BACKGROUND: Skeletal muscle depletion is common in old adults and individuals with chronic comorbidities, who have an increased risk of developing severe coronavirus disease 2019 (COVID-19), which is defined by hypoxia requiring supplemental oxygen. This study aimed to determine the association between skeletal muscle depletion and clinical outcomes in patients with severe COVID-19. METHODS: One hundred and sixteen patients with severe COVID-19 who underwent chest computed tomography scan on admission were included in this multicenter, retrospective study. Paraspinal muscle index (PMI) and radiodensity (PMD) were measured using computed tomography images. The primary composite outcome was the occurrence of critical illness (respiratory failure requiring mechanical ventilation, shock, or intensive care unit admission) or death, and the secondary outcomes were the duration of viral shedding and pulmonary fibrosis in the early rehabilitation phase. Logistic regression and Cox proportional hazards models were employed to evaluate the associations. RESULTS: The primary composite outcome occurred in 48 (41.4%) patients, who were older and had lower PMD (both p < .05). Higher PMD was associated with reduced risk of critical illness or death in a fully adjusted model overall (odds ratio [OR] per standard deviation [SD] increment: 0.87, 95% confidence interval [CI]: 0.80-0.95; p = .002) and in female patients (OR per SD increment: 0.71, 95% CI: 0.56-0.91; p = .006), although the effect was not statistically significant in male patients (p = .202). Higher PMD (hazard ratio [HR] per SD increment: 1.08, 95% CI: 1.02-1.14; p = .008) was associated with shorter duration of viral shedding among female survivors. However, no significant association was found between PMD and pulmonary fibrosis in the early rehabilitation phase, or between PMI and any outcome in both men and women. CONCLUSIONS: Higher PMD, a proxy measure of lower muscle fat deposition, was associated with a reduced risk of disease deterioration and decreased likelihood of prolonged viral shedding among female patients with severe COVID-19.

Dual-branch combination network (DCN): Towards accurate diagnosis and lesion segmentation of COVID-19 using CT images.

The recent global outbreak and spread of coronavirus disease (COVID-19) makes it an imperative to develop accurate and efficient diagnostic tools for the disease as medical resources are getting increasingly constrained. Artificial intelligence (AI)-aided tools have exhibited desirable potential; for example, chest computed tomography (CT) has been demonstrated to play a major role in the diagnosis and evaluation of COVID-19. However, developing a CT-based AI diagnostic system for the disease detection has faced considerable challenges, which is mainly due to the lack of adequate manually-delineated samples for training, as well as the requirement of sufficient sensitivity to subtle lesions in the early infection stages. In this study, we developed a dual-branch combination network (DCN) for COVID-19 diagnosis that can simultaneously achieve individual-level classification and lesion segmentation. To focus the classification branch more intensively on the lesion areas, a novel lesion attention module was developed to integrate the intermediate segmentation results. Furthermore, to manage the potential influence of different imaging parameters from individual facilities, a slice probability mapping method was proposed to learn the transformation from slice-level to individual-level classification. We conducted experiments on a large dataset of 1202 subjects from ten institutes in China. The results demonstrated that 1) the proposed DCN attained a classification accuracy of 96.74% on the internal dataset and 92.87% on the external validation dataset, thereby outperforming other models; 2) DCN obtained comparable performance with fewer samples and exhibited higher sensitivity, especially in subtle lesion detection; and 3) DCN provided good interpretability on the loci of infection compared to other deep models due to its classification guided by high-level semantic information. An online CT-based diagnostic platform for COVID-19 derived from our proposed framework is now available.

Clinical Factors Associated with Progression and Prolonged Viral Shedding in COVID-19 Patients: A Multicenter Study.

Coronavirus disease 2019 (COVID-19) is a global pandemic associated with a high mortality. Our study aimed to determine the clinical risk factors associated with disease progression and prolonged viral shedding in patients with COVID-19. Consecutive 564 hospitalized patients with confirmed COVID-19 between January 17, 2020 and February 28, 2020 were included in this multicenter, retrospective study. The effects of clinical factors on disease progression and prolonged viral shedding were analyzed using logistic regression and Cox regression analyses. 69 patients (12.2%) developed severe or critical pneumonia, with a higher incidence in the elderly and in individuals with underlying comorbidities, fever, dyspnea, and laboratory and imaging abnormalities at admission. Multivariate logistic regression analysis indicated that older age (odds ratio [OR], 1.04; 95% confidence interval [CI], 1.02-1.06), hypertension without receiving angiotensinogen converting enzyme inhibitors or angiotensin receptor blockers (ACEI/ARB) therapy (OR, 2.29; 95% CI, 1.14-4.59), and chronic obstructive pulmonary disease (OR, 7.55; 95% CI, 2.44-23.39) were independent risk factors for progression to severe or critical pneumonia. Hypertensive patients without receiving ACEI/ARB therapy showed higher lactate dehydrogenase levels and computed tomography (CT) lung scores at about 3 days after admission than those on ACEI/ARB therapy. Multivariate Cox regression analysis revealed that male gender (hazard ratio [HR], 1.22; 95% CI, 1.02-1.46), receiving lopinavir/ritonavir treatment within 7 days from illness onset (HR, 0.75; 95% CI, 0.63-0.90), and receiving systemic glucocorticoid therapy (HR, 1.79; 95% CI, 1.46-2.21) were independent factors associated with prolonged viral shedding. Our findings presented several potential clinical factors associated with developing severe or critical pneumonia and prolonged viral shedding, which may provide a rationale for clinicians in medical resource allocation and early intervention.

Early prediction of disease progression in COVID-19 pneumonia patients with chest CT and clinical characteristics.

The outbreak of coronavirus disease 2019 (COVID-19) has rapidly spread to become a worldwide emergency. Early identification of patients at risk of progression may facilitate more individually aligned treatment plans and optimized utilization of medical resource. Here we conducted a multicenter retrospective study involving patients with moderate COVID-19 pneumonia to investigate the utility of chest computed tomography (CT) and clinical characteristics to risk-stratify the patients. Our results show that CT severity score is associated with inflammatory levels and that older age, higher neutrophil-to-lymphocyte ratio (NLR), and CT severity score on admission are independent risk factors for short-term progression. The nomogram based on these risk factors shows good calibration and discrimination in the derivation and validation cohorts. These findings have implications for predicting the progression risk of COVID-19 pneumonia patients at the time of admission. CT examination may help risk-stratification and guide the timing of admission.

First CT characteristic appearance of patients with coronavirus disease 2019. / 2019å† çŠ¶ç— æ¯’ç— æ‚£è€ é¦–æ¬¡CT影像学特征.

OBJECTIVES: To investigate imaging features of the coronavirus disease 2019 (COVID-19), and to provide concrete evidences for diagnosis of COVID-19. METHODS: Imaging data of the first chest CT examination and clinical data (age, sex, clinical history, epidemiological history, and laboratory tests) of 163 patients with COVID-19 from 2 hospitals were collected for retrospective analysis. Imaging features of the first chest CT examination and the correspondence between CT manifestations and the nucleic acid test results of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) were analyzed. RESULTS: The first chest CT images of 163 COVID-19 patients showed that 92.02% of lesions were ground-glass opacity (GGO), 76.69% were consolidation, and 73.62% were GGO together with consolidation. Multiple lesions were found in 71.17% patients and multiple lobules in 86.50% patients. Lesions in 53.37% patients were found with bronchial inflation signs and those in 36.20% patients presented with "crazy paving" pattern, while only 7.36% were found with hilar node enlargement and pleural effusion. First CT findings of 18 patients were found to be inconsistent with the results of pathogen examination. CONCLUSIONS: COVID-19 patients showed specific features in the first chest CT examination. The combination of the first chest CT imaging features and SARS-CoV-2 nucleic acid test results as well as reexamination if necessary can help to make the diagnosis of SARS-CoV-2 infection accurately.