Hunting imaging biomarkers in pulmonary fibrosis: Benchmarks of the AIIB23 challenge (preprint)

Airway-related quantitative imaging biomarkers are crucial for examination, diagnosis, and prognosis in pulmonary diseases. However, the manual delineation of airway trees remains prohibitively time-consuming. While significant efforts have been made towards enhancing airway modelling, current public-available datasets concentrate on lung diseases with moderate morphological variations. The intricate honeycombing patterns present in the lung tissues of fibrotic lung disease patients exacerbate the challenges, often leading to various prediction errors. To address this issue, the 'Airway-Informed Quantitative CT Imaging Biomarker for Fibrotic Lung Disease 2023' (AIIB23) competition was organized in conjunction with the official 2023 International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI). The airway structures were meticulously annotated by three experienced radiologists. Competitors were encouraged to develop automatic airway segmentation models with high robustness and generalization abilities, followed by exploring the most correlated QIB of mortality prediction. A training set of 120 high-resolution computerised tomography (HRCT) scans were publicly released with expert annotations and mortality status. The online validation set incorporated 52 HRCT scans from patients with fibrotic lung disease and the offline test set included 140 cases from fibrosis and COVID-19 patients. The results have shown that the capacity of extracting airway trees from patients with fibrotic lung disease could be enhanced by introducing voxel-wise weighted general union loss and continuity loss. In addition to the competitive image biomarkers for prognosis, a strong airway-derived biomarker (Hazard ratio>1.5, p<0.0001) was revealed for survival prognostication compared with existing clinical measurements, clinician assessment and AI-based biomarkers.

A mechanism for SARS-CoV-2 RNA capping and its inhibition by nucleotide analogue inhibitors

Decoration of cap on viral RNA plays essential roles in SARS-CoV-2 proliferation. Here we report a mechanism for SARS-CoV-2 RNA capping and document structural details at atomic resolution. The NiRAN domain in polymerase catalyzes the covalent link of RNA 5’ end to the first residue of nsp9 (termed as RNAylation), thus being an intermediate to form cap core (GpppA) with GTP catalyzed again by NiRAN. We also reveal that triphosphorylated nucleotide analogue inhibitors can be bonded to nsp9 and fit into a previously unknown ‘Nuc-pocket’ in NiRAN, thus inhibiting nsp9 RNAylation and formation of GpppA. S-loop (residues 50-KTN-52) in NiRAN presents a remarkable conformational shift observed in RTC bound with sofosbuvir monophosphate, reasoning an ‘induce-and-lock’ mechanism to design inhibitors. These findings not only improve the understanding of SARS-CoV-2 RNA capping and the mode of action of NAIs, but also provide a strategy to design antiviral drugs. Graphical Structural analyses reveal how proteins from SARS-CoV-2 cooperate and use GTP to form the cap on viral mRNA, and how this process is interrupted by nucleotide analogues that serve as antiviral drugs.

WITHDRAWN: Clinical and CT imaging features of 2019 novel coronavirus disease (COVID-19).

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Cross-Site Severity Assessment of COVID-19 from CT Images via Domain Adaptation (preprint)

Early and accurate severity assessment of Coronavirus disease 2019 (COVID-19) based on computed tomography (CT) images offers a great help to the estimation of intensive care unit event and the clinical decision of treatment planning. To augment the labeled data and improve the generalization ability of the classification model, it is necessary to aggregate data from multiple sites. This task faces several challenges including class imbalance between mild and severe infections, domain distribution discrepancy between sites, and presence of heterogeneous features. In this paper, we propose a novel domain adaptation (DA) method with two components to address these problems. The first component is a stochastic class-balanced boosting sampling strategy that overcomes the imbalanced learning problem and improves the classification performance on poorly-predicted classes. The second component is a representation learning that guarantees three properties: 1) domain-transferability by prototype triplet loss, 2) discriminant by conditional maximum mean discrepancy loss, and 3) completeness by multi-view reconstruction loss. Particularly, we propose a domain translator and align the heterogeneous data to the estimated class prototypes (i.e., class centers) in a hyper-sphere manifold. Experiments on cross-site severity assessment of COVID-19 from CT images show that the proposed method can effectively tackle the imbalanced learning problem and outperform recent DA approaches.

A novel multiple instance learning framework for COVID-19 severity assessment via data augmentation and self-supervised learning (preprint)

How to fast and accurately assess the severity level of COVID-19 is an essential problem, when millions of people are suffering from the pandemic around the world. Currently, the chest CT is regarded as a popular and informative imaging tool for COVID-19 diagnosis. However, we observe that there are two issues -- weak annotation and insufficient data that may obstruct automatic COVID-19 severity assessment with CT images. To address these challenges, we propose a novel three-component method, i.e., 1) a deep multiple instance learning component with instance-level attention to jointly classify the bag and also weigh the instances, 2) a bag-level data augmentation component to generate virtual bags by reorganizing high confidential instances, and 3) a self-supervised pretext component to aid the learning process. We have systematically evaluated our method on the CT images of 229 COVID-19 cases, including 50 severe and 179 non-severe cases. Our method could obtain an average accuracy of 95.8%, with 93.6% sensitivity and 96.4% specificity, which outperformed previous works.

Computing Infection Distributions and Longitudinal Evolution Patterns in Lung CT Images (preprint)

Background: Spatial and temporal lung infection distributions of coronavirus disease 2019 (COVID-19) and their changes could reveal important patterns to better understand the disease and its time course. This paper presents a pipeline to analyze statistically these patterns by automatically segmenting the infection regions and registering them onto a common template. Methods: : A VB-Net is designed to automatically segment infection regions in CT images. After training and validating the model, we segmented all the CT images in the study. The segmentation results are then warped onto a pre-defined template CT image using deformable registration based on registering CT images within the lung fields. Then, the spatial distributions of infection regions and those during the course of the disease are calculated at the voxel level. Visualization and quantitative comparison can be performed between different groups. As a result, we compared the distribution maps between COVID-19 and community acquired pneumonia (CAP), between severe and critical COVID-19, and across different course of the disease. Results: : For the performance of infection segmentation, comparing the segmentation results with manually annotated ground truth, the average Dice is 91.6%±10.0%, which is close to the inter-rater difference between two radiologists (the Dice is 96.1%±3.5%). The distribution map of infection regions shows that high probability regions are in the peripheral subpleural (up to 35.1% in probability). COVID-19 GGO lesions are more widely spread than consolidations, and the latter are located more peripherally. Onset images of severe COVID-19 (inpatients) show similar lesion distributions but with smaller areas of significant difference in the right lower lobe compared to critical COVID-19 (intensive care unit patients). About the disease course, critical COVID-19 patients showed four distinct patterns (progression, absorption, enlargement, and further absorption) with remarkable concurrent HU patterns for GGO and consolidations. Conclusions: : By segmenting the infection regions with a VB-Net and registering all the CT images and the segmentation results onto a template, spatial distribution patterns of infections can be computed automatically. The algorithm provides an effective tool to visualize and quantify the spatial patterns of lung infection diseases and their changes during the disease course. Our results demonstrate different patterns between COVID-19 and CAP, between severe and critical COVID-19, as well as four distinct disease course patterns of the severe COVID-19 patients studied, with remarkable concurrent HU patterns for GGO and consolidations.

Analysis on the staff's awareness of knowledge about COVID-19 related infection and prevention in a designated hospital

OBJECTIVE: To study the awareness of knowledge of 2019 coronavirus disease(COVID-19) prevention and control among the staff in a designated hospital. Providing basis for the improvement on methods and contents in the further hospital infection training. METHODS: A questionnaire was designed to survey the designated hospital staff who participated in the treatment group from Jan. 25 th to Feb. 10 th, 2020. The data were described by the number and percent. RESULTS: Totally 702 questionnaires were sent out, 694 questionnaires were valid(98.86%). The qualified rate of staff's overall awareness of knowledge was 84.01%, in different age groups, the highest qualified rate was 31-40 years group(89.33%), the lowest was 18-30 years group(76.68%), the difference was significant(P<0.05);in different lines of staff, the highest qualified rate was second-line(95.92%), the lowest was first-line(73.33%), the difference was significant(P<0.001). Multivariate logistic regression analysis showed that: compared with the first-line staff, the second-line staff and the third-line staff were 0.12 and 0.47 times of the risk of unqualified awareness of knowledge, respectively;compared with the staff younger than 30 years, the risk of unqualified awareness of knowledge of the staff aged 31-40 was 0.42 times. CONCLUSION: The staff of the designated hospital of COVID-19 have a good understanding of the general healthcare infection knowledge, but the front-line staff need to strengthen their learning much more.

Assessing Road Traffic Safety During COVID-19: Inequality, Irregularity, and Severity (preprint)

COVID-19 is affecting every social sector significantly, including human mobility and subsequently road traffic safety. In this study, we analyze the impact of the pandemic on traffic accidents using two cities, namely Los Angeles and New York City in the U.S., as examples. Specifically, we have analyzed traffic accidents associated with various demographic groups, how traffic accidents are distributed in time and space, and the severity level of traffic accidents that both involve and do not involve other transportation modes (e.g., pedestrians and motorists). We have made the following observations: 1) the pandemic has disproportionately affected certain age groups, races, and genders; 2) the "hotspots" of traffic accidents have been shifted in both time and space compared to time periods that are prior to the pandemic, demonstrating irregularity; and 3) the number of non-fatal accident cases has decreased but the number of severe and fatal cases of traffic accidents remains the same under the pandemic.

Artificial Intelligence-based Disease Surveillance Amid COVID-19 and Beyond: A Systematic Review Protocol (preprint)

Background: Infectious diseases are dangerous and deadly. As the leading causes of morbidity and mortality in all demographics across the world, infectious diseases carry substantial social, economic, and healthcare costs. Unlike previous global health crises, health experts now have access to more advanced tools and techniques to understand pandemics like COVID-19 better and faster; one such class of tools is artificial intelligence (AI) enabled disease surveillance systems. AI-based surveillance systems allow health experts to perform rapid mass infection prediction to identify potential disease cases, which is integral to understanding transmission and curbing the spread of the pandemic. However, while the importance of AI-based disease surveillance mechanisms in pandemic control is clear, what is less known is the state-of-the-art application of these mechanisms in countries across the world. Therefore, to bridge this gap, we aim to systematically review the literature to identify (1) how AI-based disease surveillance systems have been applied in counties worldwide amid COVID-19, (2) the characteristics and effects of these applications regarding the control of the spread of COVID-19, and (3) what additional disease surveillance resources such as database, AI-based tools and techniques that can be further added to the current toolbox in the fight against COVID-19. Methods: To locate research on AI-based disease surveillance amid COVID-19, we will search databases including PubMed, IEEE Explore, ACM Digital Library, and Science Direct to identify all potential records. Titles, abstracts, and full-text articles were screened against eligibility criteria developed a priori. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures was adopted as the reporting framework.Results: NA for now Conclusions: Findings of our study will fill an important void in the literature, as no research has systematically reviewed available AI-based disease surveillance in the context of COVID-19. As the world continues to reel from COVID-19, it is important to identify cost-effective AI-based disease surveillance mechanisms that can detect COVID-19 cases and explain how one COVID-19 case turns into many cases, so that better prevention measures can be established to curb the spread of the COVID pandemic in a timely manner.   Study Protocol Registration: PROSPERO CRD42020204992

Evidence-based health solutions for challenges women face during pandemics like COVID-19: A systematic review protocol (preprint)

Background One of the most vulnerable populations to COVID-19 is women. Multiple factors associated with violence against women (i.e. sexual assault, domestic violence, homelessness) create an increased vulnerability for women during the COVID pandemic. Women also constitute the majority of older nursing home residents and healthcare workers (e.g., nurses), who have the most pronounced exposure to COVID-19. These factors combined with resource restraints like rationing and lack of access to healthcare can further exacerbate women’s physical and psychological health issues. While literature has well-documented challenges that women face during COVID-19, there is a lack of evidence-based solutions that have the potential to mitigate these difficulties. Therefore, to address this issue, we aim to conduct a systematic review of the literature to: (1) identify interventions designed for women in the context of pandemics, (2) describe the characteristics and effects of these interventions concerning the distinctive traits of women and pandemics, and (3) present evidence-based health solutions for women to mitigate challenges they face amid and beyond COVID-19.Methods A systematic review of literature will be conducted on databases including PubMed, PsycINFO on the EBSCO platform, CINAHL on the EBSCO platform, and Scopus, based on a search strategy developed in consultation with an experienced medical librarian. Titles, abstracts, and full-text articles will be screened against eligibility criteria developed a priori. The Preferred Reporting Items for Systematic Reviews and Meta-Analyses procedures will be adopted as the reporting framework, and data extracted (e.g., intervention details) will be evaluated by a multidisciplinary research team.Results NA for now—This is a protocol study.Conclusions Findings of this study will fill an important void in the literature. Considering that, in times of pandemic, women are especially subject to grim health disparities, like pronounced exposure to COVID-19, reproductive health issues, elevated domestic violence, increased mental health challenges, and lack of access to healthcare services, the need for evidence-based health solutions that could address these unique challenges is of paramount importance. A comprehensive understanding of the characteristics and effects of health solutions available to women in the context of pandemics can also help researchers identify areas of improvement regarding intervention design and development. This may further safeguard women’s health and wellbeing amid pandemics like COVID-19 and beyond.Study Protocol Registration: PROSPERO CRD42020194003

The Value of High-flow Nasal Cannula Oxygen Therapy in Treating Patients With Severe Novel Coronavirus Pneumonia (preprint)

Objective This study aimed to investigate the value of high-flow nasal cannula (HNFC) oxygen therapy in treating patients with severe novel coronavirus pneumonia (COVID-19).Methods The clinical data of 22 patients with severe COVID-19 were collected. The heart rate (HR), respiratory rate (RR) and oxygenation index (PO2/FiO2) at 0, 6, 24 and 72 hours after treatment were compared between the HFNC oxygen therapy group and the conventional oxygen therapy (COT) group. In addition, the white blood cell (WBC) count, lymphocyte (L) count, C-reactive protein (CRP) and procalcitonin (PCT) were compared before and at 72 hours after oxygen therapy treatment.Results Of the included patients, 12 were assigned to the HFNC oxygen therapy group and 10 were assigned to the COT group. The differences in HR, RR, PaO2/FiO2, WBC, L, CRP and PCT at 0 hours between the two groups were not statistically significant. At 6 hours after treatment with the two oxygen therapies, HR, RR and PaO2/FiO2 were better in the HFNC oxygen therapy group than in the COT group (p < 0.05), while at 24 and 72 hours after treatment with the two oxygen therapies, PaO2/FiO2 was better in the HFNC oxygen therapy group than in the COT group (p < 0.05), but the differences in HR and RR were not statistically significant. At 72 hours after treatment, L and CRP had significantly improved in the HFNC oxygen therapy group compared with the COT group, but the differences in WBC and PCT were not statistically significant. The length of stay in the intensive care unit (ICU) and the total length of hospitalization were shorter in the HFNC oxygen therapy group than in the COT group, and the differences between the two groups were statistically significant.Conclusion Compared with COT, early application of HFNC oxygen therapy in patients with severe COVID-19 can significantly improve oxygenation and RR, and HFNC oxygen therapy can improve the infection indexes of patients and reduce the length of stay in the ICU of patients. Therefore, it has high clinical application value.

Guideline for diagnosis and treatment of spine trauma in the epidemic of COVID-19.

Outbreak of COVID-19 is ongoing all over the world. Spine trauma is one of the most common types of trauma and will probably be encountered during the fight against COVID-19 and resumption of work and production. Patients with unstable spine fractures or continuous deterioration of neurological function require emergency surgery. The COVID-19 epidemic has brought tremendous challenges to the diagnosis and treatment of such patients. To coordinate the diagnosis and treatment of infectious disease prevention and spine trauma so as to formulate a rigorous diagnosis and treatment plan and to reduce the disability and mortality of the disease, multidisciplinary collaboration is needed. This expert consensus is formulated in order to (1) prevent and control the epidemic, (2) diagnose and treat patients with spine trauma reasonably, and (3) reduce the risk of cross-infection between patients and medical personnel during the treatment.

Synergistic Learning of Lung Lobe Segmentation and Hierarchical Multi-Instance Classification for Automated Severity Assessment of COVID-19 in CT Images (preprint)

Understanding chest CT imaging of the coronavirus disease 2019 (COVID-19) will help detect infections early and assess the disease progression. Especially, automated severity assessment of COVID-19 in CT images plays an essential role in identifying cases that are in great need of intensive clinical care. However, it is often challenging to accurately assess the severity of this disease in CT images, due to variable infection regions in the lungs, similar imaging biomarkers, and large inter-case variations. To this end, we propose a synergistic learning framework for automated severity assessment of COVID-19 in 3D CT images, by jointly performing lung lobe segmentation and multi-instance classification. Considering that only a few infection regions in a CT image are related to the severity assessment, we first represent each input image by a bag that contains a set of 2D image patches (with each cropped from a specific slice). A multi-task multi-instance deep network (called M$^2$UNet) is then developed to assess the severity of COVID-19 patients and also segment the lung lobe simultaneously. Our M$^2$UNet consists of a patch-level encoder, a segmentation sub-network for lung lobe segmentation, and a classification sub-network for severity assessment (with a unique hierarchical multi-instance learning strategy). Here, the context information provided by segmentation can be implicitly employed to improve the performance of severity assessment. Extensive experiments were performed on a real COVID-19 CT image dataset consisting of 666 chest CT images, with results suggesting the effectiveness of our proposed method compared to several state-of-the-art methods.

Joint Prediction and Time Estimation of COVID-19 Developing Severe Symptoms using Chest CT Scan (preprint)

With the rapidly worldwide spread of Coronavirus disease (COVID-19), it is of great importance to conduct early diagnosis of COVID-19 and predict the time that patients might convert to the severe stage, for designing effective treatment plan and reducing the clinicians' workloads. In this study, we propose a joint classification and regression method to determine whether the patient would develop severe symptoms in the later time, and if yes, predict the possible conversion time that the patient would spend to convert to the severe stage. To do this, the proposed method takes into account 1) the weight for each sample to reduce the outliers' influence and explore the problem of imbalance classification, and 2) the weight for each feature via a sparsity regularization term to remove the redundant features of high-dimensional data and learn the shared information across the classification task and the regression task. To our knowledge, this study is the first work to predict the disease progression and the conversion time, which could help clinicians to deal with the potential severe cases in time or even save the patients' lives. Experimental analysis was conducted on a real data set from two hospitals with 422 chest computed tomography (CT) scans, where 52 cases were converted to severe on average 5.64 days and 34 cases were severe at admission. Results show that our method achieves the best classification (e.g., 85.91% of accuracy) and regression (e.g., 0.462 of the correlation coefficient) performance, compared to all comparison methods. Moreover, our proposed method yields 76.97% of accuracy for predicting the severe cases, 0.524 of the correlation coefficient, and 0.55 days difference for the converted time.

Adaptive Feature Selection Guided Deep Forest for COVID-19 Classification with Chest CT (preprint)

Chest computed tomography (CT) becomes an effective tool to assist the diagnosis of coronavirus disease-19 (COVID-19). Due to the outbreak of COVID-19 worldwide, using the computed-aided diagnosis technique for COVID-19 classification based on CT images could largely alleviate the burden of clinicians. In this paper, we propose an Adaptive Feature Selection guided Deep Forest (AFS-DF) for COVID-19 classification based on chest CT images. Specifically, we first extract location-specific features from CT images. Then, in order to capture the high-level representation of these features with the relatively small-scale data, we leverage a deep forest model to learn high-level representation of the features. Moreover, we propose a feature selection method based on the trained deep forest model to reduce the redundancy of features, where the feature selection could be adaptively incorporated with the COVID-19 classification model. We evaluated our proposed AFS-DF on COVID-19 dataset with 1495 patients of COVID-19 and 1027 patients of community acquired pneumonia (CAP). The accuracy (ACC), sensitivity (SEN), specificity (SPE) and AUC achieved by our method are 91.79%, 93.05%, 89.95% and 96.35%, respectively. Experimental results on the COVID-19 dataset suggest that the proposed AFS-DF achieves superior performance in COVID-19 vs. CAP classification, compared with 4 widely used machine learning methods.

Hypergraph Learning for Identification of COVID-19 with CT Imaging (preprint)

The coronavirus disease, named COVID-19, has become the largest global public health crisis since it started in early 2020. CT imaging has been used as a complementary tool to assist early screening, especially for the rapid identification of COVID-19 cases from community acquired pneumonia (CAP) cases. The main challenge in early screening is how to model the confusing cases in the COVID-19 and CAP groups, with very similar clinical manifestations and imaging features. To tackle this challenge, we propose an Uncertainty Vertex-weighted Hypergraph Learning (UVHL) method to identify COVID-19 from CAP using CT images. In particular, multiple types of features (including regional features and radiomics features) are first extracted from CT image for each case. Then, the relationship among different cases is formulated by a hypergraph structure, with each case represented as a vertex in the hypergraph. The uncertainty of each vertex is further computed with an uncertainty score measurement and used as a weight in the hypergraph. Finally, a learning process of the vertex-weighted hypergraph is used to predict whether a new testing case belongs to COVID-19 or not. Experiments on a large multi-center pneumonia dataset, consisting of 2,148 COVID-19 cases and 1,182 CAP cases from five hospitals, are conducted to evaluate the performance of the proposed method. Results demonstrate the effectiveness and robustness of our proposed method on the identification of COVID-19 in comparison to state-of-the-art methods.

Dual-Sampling Attention Network for Diagnosis of COVID-19 from Community Acquired Pneumonia (preprint)

The coronavirus disease (COVID-19) is rapidly spreading all over the world, and has infected more than 1,436,000 people in more than 200 countries and territories as of April 9, 2020. Detecting COVID-19 at early stage is essential to deliver proper healthcare to the patients and also to protect the uninfected population. To this end, we develop a dual-sampling attention network to automatically diagnose COVID- 19 from the community acquired pneumonia (CAP) in chest computed tomography (CT). In particular, we propose a novel online attention module with a 3D convolutional network (CNN) to focus on the infection regions in lungs when making decisions of diagnoses. Note that there exists imbalanced distribution of the sizes of the infection regions between COVID-19 and CAP, partially due to fast progress of COVID-19 after symptom onset. Therefore, we develop a dual-sampling strategy to mitigate the imbalanced learning. Our method is evaluated (to our best knowledge) upon the largest multi-center CT data for COVID-19 from 8 hospitals. In the training-validation stage, we collect 2186 CT scans from 1588 patients for a 5-fold cross-validation. In the testing stage, we employ another independent large-scale testing dataset including 2796 CT scans from 2057 patients. Results show that our algorithm can identify the COVID-19 images with the area under the receiver operating characteristic curve (AUC) value of 0.944, accuracy of 87.5%, sensitivity of 86.9%, specificity of 90.1%, and F1-score of 82.0%. With this performance, the proposed algorithm could potentially aid radiologists with COVID-19 diagnosis from CAP, especially in the early stage of the COVID-19 outbreak.

Diagnosis of Coronavirus Disease 2019 (COVID-19) with Structured Latent Multi-View Representation Learning (preprint)

Recently, the outbreak of Coronavirus Disease 2019 (COVID-19) has spread rapidly across the world. Due to the large number of affected patients and heavy labor for doctors, computer-aided diagnosis with machine learning algorithm is urgently needed, and could largely reduce the efforts of clinicians and accelerate the diagnosis process. Chest computed tomography (CT) has been recognized as an informative tool for diagnosis of the disease. In this study, we propose to conduct the diagnosis of COVID-19 with a series of features extracted from CT images. To fully explore multiple features describing CT images from different views, a unified latent representation is learned which can completely encode information from different aspects of features and is endowed with promising class structure for separability. Specifically, the completeness is guaranteed with a group of backward neural networks (each for one type of features), while by using class labels the representation is enforced to be compact within COVID-19/community-acquired pneumonia (CAP) and also a large margin is guaranteed between different types of pneumonia. In this way, our model can well avoid overfitting compared to the case of directly projecting highdimensional features into classes. Extensive experimental results show that the proposed method outperforms all comparison methods, and rather stable performances are observed when varying the numbers of training data.