A convolutional neural network with pixel-wise sparse graph reasoning for COVID-19 lesion segmentation in CT images.

The COVID-19 pandemic has extremely threatened human health, and automated algorithms are needed to segment infected regions in the lung using computed tomography (CT). Although several deep convolutional neural networks (DCNNs) have proposed for this purpose, their performance on this task is suppressed due to the limited local receptive field and deficient global reasoning ability. To address these issues, we propose a segmentation network with a novel pixel-wise sparse graph reasoning (PSGR) module for the segmentation of COVID-19 infected regions in CT images. The PSGR module, which is inserted between the encoder and decoder of the network, can improve the modeling of global contextual information. In the PSGR module, a graph is first constructed by projecting each pixel on a node based on the features produced by the encoder. Then, we convert the graph into a sparsely-connected one by keeping K strongest connections to each uncertainly segmented pixel. Finally, the global reasoning is performed on the sparsely-connected graph. Our segmentation network was evaluated on three publicly available datasets and compared with a variety of widely-used segmentation models. Our results demonstrate that (1) the proposed PSGR module can capture the long-range dependencies effectively and (2) the segmentation model equipped with this PSGR module can accurately segment COVID-19 infected regions in CT images and outperform all other competing models.

BiT-MAC: Mortality prediction by bidirectional time and multi-feature attention coupled network on multivariate irregular time series.

Mortality prediction is crucial to evaluate the severity of illness and assist in improving the prognosis of patients. In clinical settings, one way is to analyze the multivariate time series (MTSs) of patients based on their medical data, such as heart rates and invasive mean arterial blood pressure. However, this suffers from sparse, irregularly sampled, and incomplete data issues. These issues can compromise the performance of follow-up MTS-based analytic applications. Plenty of existing methods try to deal with such irregular MTSs with missing values by capturing the temporal dependencies within a time series, yet in-depth research on modeling inter-MTS couplings remains rare and lacks model interpretability. To this end, we propose a bidirectional time and multi-feature attention coupled network (BiT-MAC) to capture the temporal dependencies (i.e., intra-time series coupling) and the hidden relationships among variables (i.e., inter-time series coupling) with a bidirectional recurrent neural network and multi-head attention, respectively. The resulting intra- and inter-time series coupling representations are then fused to estimate the missing values for a more robust MTS-based prediction. We evaluate BiT-MAC by applying it to the missing-data corrupted mortality prediction on two real-world clinical datasets, i.e., PhysioNet'2012 and COVID-19. Extensive experiments demonstrate the superiority of BiT-MAC over cutting-edge models, verifying the great value of the deep and hidden relations captured by MTSs. The interpretability of features is further demonstrated through a case study.

Domain and Content Adaptive Convolution based Multi-Source Domain Generalization for Medical Image Segmentation.

The domain gap caused mainly by variable medical image quality renders a major obstacle on the path between training a segmentation model in the lab and applying the trained model to unseen clinical data. To address this issue, domain generalization methods have been proposed, which however usually use static convolutions and are less flexible. In this paper, we propose a multi-source domain generalization model based on the domain and content adaptive convolution (DCAC) for the segmentation of medical images across different modalities. Specifically, we design the domain adaptive convolution (DAC) module and content adaptive convolution (CAC) module and incorporate both into an encoder-decoder backbone. In the DAC module, a dynamic convolutional head is conditioned on the predicted domain code of the input to make our model adapt to the unseen target domain. In the CAC module, a dynamic convolutional head is conditioned on the global image features to make our model adapt to the test image. We evaluated the DCAC model against the baseline and four state-of-the-art domain generalization methods on the prostate segmentation, COVID-19 lesion segmentation, and optic cup/optic disc segmentation tasks. Our results not only indicate that the proposed DCAC model outperforms all competing methods on each segmentation task but also demonstrate the effectiveness of the DAC and CAC modules. Code is available at https://git.io/DCAC.

Rapid artificial intelligence solutions in a pandemic-The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

SC2Net: A Novel Segmentation-Based Classification Network for Detection of COVID-19 in Chest X-Ray Images.

The pandemic of COVID-19 has become a global crisis in public health, which has led to a massive number of deaths and severe economic degradation. To suppress the spread of COVID-19, accurate diagnosis at an early stage is crucial. As the popularly used real-time reverse transcriptase polymerase chain reaction (RT-PCR) swab test can be lengthy and inaccurate, chest screening with radiography imaging is still preferred. However, due to limited image data and the difficulty of the early-stage diagnosis, existing models suffer from ineffective feature extraction and poor network convergence and optimisation. To tackle these issues, a segmentation-based COVID-19 classification network, namely SC2Net, is proposed for effective detection of the COVID-19 from chest x-ray (CXR) images. The SC2Net consists of two subnets: a COVID-19 lung segmentation network (CLSeg), and a spatial attention network (SANet). In order to supress the interference from the background, the CLSeg is first applied to segment the lung region from the CXR. The segmented lung region is then fed to the SANet for classification and diagnosis of the COVID-19. As a shallow yet effective classifier, SANet takes the ResNet-18 as the feature extractor and enhances high-level feature via the proposed spatial attention module. For performance evaluation, the COVIDGR 1.0 dataset is used, which is a high-quality dataset with various severity levels of the COVID-19. Experimental results have shown that, our SC2Net has an average accuracy of 84.23% and an average F1 score of 81.31% in detection of COVID-19, outperforming several state-of-the-art approaches.

Prevalence and associated factors of suicidal ideation among college students during the COVID-19 pandemic in China: a 3-wave repeated survey.

OBJECTIVE: The coronavirus disease 2019 (COVID-19) pandemic, a major public health crisis, harms individuals' mental health. This 3-wave repeated survey aimed to examine the prevalence and correlates of suicidal ideation at different stages of the COVID-19 pandemic in a large sample of college students in China. METHODS: Using a repeated cross-sectional survey design, we conducted 3 online surveys of college students during the COVID-19 pandemic at 22 universities in Guandong, China. The 3 surveys were conducted during the outbreak period (T1: 3 February to 10 February 2020, N = 164,101), remission period (T2: 24 March to 3 April 2020, N = 148,384), and normalized prevention and control period (T3: 1 June to 15 June 2020, N = 159,187). Suicidal ideation was measured by the ninth item of the Patient Health Questionnaire-9. A range of suicide-related factors was assessed, including sociodemographic characteristics, depression, anxiety, insomnia, pre-existing mental health problems, and COVID-19-related factors. RESULTS: The prevalence of suicidal ideation was 8.5%, 11.0% and 12.6% at T1, T2, and T3, respectively. Male sex (aOR: 1.35-1.44, Ps < 0.001), poor self-perceived mental health (aOR: 2.25-2.81, Ps < 0.001), mental diseases (aOR: 1.52-2.09, P < 0.001), prior psychological counseling (aOR: 1.23-1.37, Ps < 0.01), negative perception of the risk of the COVID-19 epidemic (aOR: 1.14-1.36, Ps < 0.001), depressive symptoms (aOR: 2.51-303, Ps < 0.001) and anxiety symptoms (aOR: 1.62-101.11, Ps < 0.001) were associated with an increased risk of suicidal ideation. CONCLUSION: Suicidal ideation appeared to increase during the COVID-19 pandemic remission period among college students in China. Multiple factors, especially mental health problems, are associated with suicidal ideation. Psychosocial interventions should be implemented during and after the COVID-19 pandemic to reduce suicide risk among college students.

Multiplexed Lab-on-a-Chip Bioassays for Testing Antibodies against SARS-CoV-2 and Its Variants in Multiple Individuals.

Neutralization assays that can measure neutralizing antibodies in serum are vital for large-scale serodiagnosis and vaccine evaluation. Here, we establish multiplexed lab-on-a-chip bioassays for testing antibodies against severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and its variants. Compared with enzyme-linked immunosorbent assay (ELISA), our method exhibits a low consumption of sample and reagents (10 µL), a low limit of detection (LOD: 0.08 ng/mL), a quick sample-to-answer time (about 70 min), and multiplexed ability (5 targets in each of 7 samples in one assay). We can also increase the throughput as needed. The concentrations of antibodies against RBD, D614G, N501Y, E484K, and L452R/E484Q-mutants after two doses of vaccines are 6.6 ± 3.6, 8.7 ± 4.6, 3.4 ± 2.8, 3.8 ± 2.8, and 2.8 ± 2.3 ng/mL, respectively. This suggests that neutralizing activities against N501Y, E484K, and L452R/E484Q-mutants were less effective than RBD and D614G-mutant. We performed a plaque reduction neutralization test (PRNT) for all volunteers. Compared with PRNT, our assay is fast, accurate, inexpensive, and multiplexed with multiple-sample processing ability, which is good for large-scale serodiagnosis and vaccine evaluation.

Pediatric asthma control during the COVID-19 pandemic: A systematic review and meta-analysis.

BACKGROUND: With the onset of the coronavirus disease 2019 (COVID-19) pandemic, many experts expected that asthma-associated morbidity because of severe acute respiratory syndrome coronavirus 2 infection would dramatically increase. However, some studies suggested that there was no apparent increasing in asthma-related morbidity in children with asthma, it is even possible children may have improved outcomes. To understand the relationship between the COVID-19 pandemic and asthma outcomes, we performed this article. METHODS: We searched PubMed, Embase, and Cochrane Library to find literature from December 2019 to June 2021 related to COVID-19 and children's asthma control, among which results such as abstracts, comments, letters, reviews, and case reports were excluded. The level of asthma control during the COVID-19 pandemic was synthesized and discussed by outcomes of asthma exacerbation, emergency room visit, asthma admission, and childhood asthma control test (c-ACT). RESULTS: A total of 22,159 subjects were included in 10 studies. Random effect model was used to account for the data. Compared with the same period before the COVID-19 pandemic, asthma exacerbation reduced (odds ratio [OR] = 0.26, 95% confidence interval [CI] = [0.14-0.48], Z = 4.32, p < 0.0001), the odds of emergency room visit decreased as well (OR = 0.11, 95% CI = [0.04-0.26], Z = 4.98, p < 0.00001). The outcome of asthma admission showed no significant difference (OR = 0.84, 95% CI = [0.32-2.20], Z = 0.36, p = 0.72). The outcome of c-ACT scores were not analyzed because of the different manifestations used. Overall, c-ACT scores reduced during the pandemic. CONCLUSION: Compared to the same period before the COVID-19 pandemic, the level of asthma control has been significantly improved. We need to understand the exact factors leading to these improvements and find methods to sustain it.

Rapid Artificial Intelligence Solutions in a Pandemic - The COVID-19-20 Lung CT Lesion Segmentation Challenge.

Artificial intelligence (AI) methods for the automatic detection and quantification of COVID-19 lesions in chest computed tomography (CT) might play an important role in the monitoring and management of the disease. We organized an international challenge and competition for the development and comparison of AI algorithms for this task, which we supported with public data and state-of-the-art benchmark methods. Board Certified Radiologists annotated 295 public images from two sources (A and B) for algorithms training (n=199, source A), validation (n=50, source A) and testing (n=23, source A; n=23, source B). There were 1,096 registered teams of which 225 and 98 completed the validation and testing phases, respectively. The challenge showed that AI models could be rapidly designed by diverse teams with the potential to measure disease or facilitate timely and patient-specific interventions. This paper provides an overview and the major outcomes of the COVID-19 Lung CT Lesion Segmentation Challenge - 2020.

Viral Pneumonia Screening on Chest X-Rays Using Confidence-Aware Anomaly Detection.

Clusters of viral pneumonia occurrences over a short period may be a harbinger of an outbreak or pandemic. Rapid and accurate detection of viral pneumonia using chest X-rays can be of significant value for large-scale screening and epidemic prevention, particularly when other more sophisticated imaging modalities are not readily accessible. However, the emergence of novel mutated viruses causes a substantial dataset shift, which can greatly limit the performance of classification-based approaches. In this paper, we formulate the task of differentiating viral pneumonia from non-viral pneumonia and healthy controls into a one-class classification-based anomaly detection problem. We therefore propose the confidence-aware anomaly detection (CAAD) model, which consists of a shared feature extractor, an anomaly detection module, and a confidence prediction module. If the anomaly score produced by the anomaly detection module is large enough, or the confidence score estimated by the confidence prediction module is small enough, the input will be accepted as an anomaly case (i.e., viral pneumonia). The major advantage of our approach over binary classification is that we avoid modeling individual viral pneumonia classes explicitly and treat all known viral pneumonia cases as anomalies to improve the one-class model. The proposed model outperforms binary classification models on the clinical X-VIRAL dataset that contains 5,977 viral pneumonia (no COVID-19) cases, 37,393 non-viral pneumonia or healthy cases. Moreover, when directly testing on the X-COVID dataset that contains 106 COVID-19 cases and 107 normal controls without any fine-tuning, our model achieves an AUC of 83.61% and sensitivity of 71.70%, which is comparable to the performance of radiologists reported in the literature.