COVID-19 imaging, where do we go from here? Bibliometric analysis of medical imaging in COVID-19.

OBJECTIVES: We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging to determine the current status and indicate possible future directions. METHODS: This research provides an analysis of Web of Science Core Collection (WoSCC) indexed articles on COVID-19 and medical imaging published between 1 January 2020 and 30 June 2022, using the search terms "COVID-19" and medical imaging terms (such as "X-ray" or "CT"). Publications based solely on COVID-19 themes or medical image themes were excluded. CiteSpace was used to identify the predominant topics and generate a visual map of countries, institutions, authors, and keyword networks. RESULTS: The search included 4444 publications. The journal with the most publications was European Radiology, and the most co-cited journal was Radiology. China was the most frequently cited country in terms of co-authorship, with the Huazhong University of Science and Technology being the institution contributing with the highest number of relevant co-authorships. Research trends and leading topics included: assessment of initial COVID-19-related clinical imaging features, differential diagnosis using artificial intelligence (AI) technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. CONCLUSIONS: This bibliometric analysis of COVID-19-related medical imaging helps clarify the current research situation and developmental trends. Subsequent trends in COVID-19 imaging are likely to shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases. Key Points • We conducted a systematic and comprehensive bibliometric analysis of COVID-19-related medical imaging from 1 January 2020 to 30 June 2022. • Research trends and leading topics included assessment of initial COVID-19-related clinical imaging features, differential diagnosis using AI technology and model interpretability, diagnosis systems construction, COVID-19 vaccination, complications, and predicting prognosis. • Future trends in COVID-19-related imaging are likely to involve a shift from lung structure to function, from lung tissue to other related organs, and from COVID-19 to the impact of COVID-19 on the diagnosis and treatment of other diseases.

An artificial intelligence-based radiomics model for differential diagnosis between coronavirus disease 2019 and other viral pneumonias

OBJECTIVE: To set up a differential diagnosis radiomics model to identify coronavirus disease 2019 (COVID-19) and other viral pneumonias based on an artificial intelligence (AI) approach that utilizes computed tomography (CT) images. MATERIALS AND METHODS: This retrospective multi-center research involved 225 patients with COVID-19 and 265 patients with other viral pneumonias. The least absolute shrinkage and selection operator algorithm was used for the optimized features selection from 1218 radiomics features. Finally, a logistic regression (LR) classifier was applied to construct different diagnosis models. The receiver operating characteristic curve analysis was applied to evaluate the accuracy of different models. RESULTS: The patients were divided into a training set (313 of 392, 80%), an internal test set (79 of 392, 20%) and an external test set (n = 98). Thirteen features were selected to build the machine learning-based CT radiomics models. LR classifiers performed well in the training set (area under the curve [AUC] = 0.91), internal test set (AUC = 0.94), and external test set (AUC = 0.91). Delong tests suggested there was no significant difference between training and the two test sets (P > 0.05). CONCLUSION: The use of an AI-based radiomics model enables rapid discrimination of patients with COVID-19 from other viral infections, which can aid better surveillance and control during a pneumonia outbreak.

An AI-based auxiliary empirical antibiotic therapy model for children with bacterial pneumonia using low-dose chest CT images.

PURPOSE: To construct an auxiliary empirical antibiotic therapy (EAT) multi-class classification model for children with bacterial pneumonia using radiomics features based on artificial intelligence and low-dose chest CT images. MATERIALS AND METHODS: Data were retrospectively collected from children with pathogen-confirmed bacterial pneumonia including Gram-positive bacterial pneumonia (122/389, 31%), Gram-negative bacterial pneumonia (159/389, 41%) and atypical bacterial pneumonia (108/389, 28%) from January 1 to June 30, 2019. Nine machine-learning models were separately evaluated based on radiomics features extracted from CT images; three optimal submodels were constructed and integrated to form a multi-class classification model. RESULTS: We selected five features to develop three radiomics submodels: a Gram-positive model, a Gram-negative model and an atypical model. The comprehensive radiomics model using support vector machine method yielded an average area under the curve (AUC) of 0.75 [95% confidence interval (CI), 0.65-0.83] and accuracy (ACC) of 0.58 [sensitivity (SEN), 0.57; specificity (SPE), 0.78] in the training set, and an average AUC of 0.73 (95% CI 0.61-0.79) and ACC of 0.54 (SEN, 0.52; SPE, 0.75) in the test set. CONCLUSION: This auxiliary EAT radiomics multi-class classification model was deserved to be researched in differential diagnosing bacterial pneumonias in children.

An AI-based radiomics nomogram for disease prognosis in patients with COVID-19 pneumonia using initial CT images and clinical indicators.

BACKGROUND: This study utilized a comprehensive nomogram to evaluate the prognosis of patients with COVID-19 pneumonia. METHODS: COVID-19 pneumonia data was divided into training set (256 of 321, 80%), internal validation set (65 of 321, 20%) and independent external validation set (n = 188). After image processing, lesion segmentation, feature extraction and feature selection, radiomics signatures and clinical indicators were used to develop a radiomics model and a clinical model respectively. Combining radiomics signatures and clinical indicators, a radiomics nomogram was built. The performance of proposed models was evaluated by the receiver operating characteristic curve (AUC). Calibration curves and decision curve analysis were used to assess the performance of the radiomics nomogram. RESULTS: Two clinical indicators that were age and chronic lung disease or asthma and 21 radiomics features were selected to build the radiomics nomogram. The radiomics nomogram yielded an Area Under The Curve1 (AUC) of 0.88 and accuracy of 0.80 in the training set, an AUC of 0.85 and accuracy of 0.77 in internal testing validation set and an AUC of 0.84 and accuracy of 0.75 in independent external validation set. The performance of radiomics nomogram was better than clinical model (AUC = 0.77, p < 0.001) and radiomics model (AUC = 0.72, p = 0.025) in independent external validation set. CONCLUSIONS: The radiomics nomogram may be used to assess the deterioration of COVID-19 pneumonia.

An HIV-infected patient with coronavirus disease 2019 has a favourable prognosis: a case report.

The coronavirus disease 2019 (COVID-19), caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly, which now has turned into a pandemic. The new emerging infectious disease has raised many challenges and uncertainties regarding disease management and prognosis in immunocompromised patient populations. The risk of COVID-19 among people living with human immunodeficiency virus (HIV) has different opinions. Some scholars speculated that patients with HIV may be at decreased risk for complications of COVID-19 because HIV antiretroviral medications may have activity against coronaviruses such as SARS-CoV-2. But others have the opposite because of the immunosuppression for HIV patients. Here we reported a case of HIV-infected patient confirmed with COVID-19 and had a favourable prognosis. The patient was a 24-year-old male who was diagnosed with HIV infection 2 years ago and then followed a regular antiretroviral therapy (ART). After infected with COVID-19, the patient had no other clinical symptoms and laboratory abnormalities throughout the course of the disease except presented with fever for a short-term (2 days), and no secondary infection or exacerbation occurred after admission in hospital. Follow-up chest CT showed that the lung lesions disappeared within a short period of time. After standard treatment by 9 days, the patient was cured and discharged. This report highlights the importance of ART for HIV-infected persons, and with regular ART for HIV patients may reduce adverse consequences after infection with COVID-19.

COVID-19: A review of what radiologists need to know.

Coronavirus disease-2019 (COVID-19) is spreading throughout the world. Chest radiography and computed tomography play an important role in disease diagnosis, differential diagnosis, severity evaluation, prognosis prediction, therapeutic effects assessment and follow-up of patients with COVID-19. In this review, we summarize knowledge of COVID-19 pneumonia that may help improve the abilities of radiologists to diagnose and evaluate this highly infectious disease, which is essential for epidemic control and preventing new outbreaks in the short term.

FaNet: fast assessment network for the novel coronavirus (COVID-19) pneumonia based on 3D CT imaging and clinical symptoms.

The novel coronavirus (COVID-19) pneumonia has become a serious health challenge in countries worldwide. Many radiological findings have shown that X-ray and CT imaging scans are an effective solution to assess disease severity during the early stage of COVID-19. Many artificial intelligence (AI)-assisted diagnosis works have rapidly been proposed to focus on solving this classification problem and determine whether a patient is infected with COVID-19. Most of these works have designed networks and applied a single CT image to perform classification; however, this approach ignores prior information such as the patient's clinical symptoms. Second, making a more specific diagnosis of clinical severity, such as slight or severe, is worthy of attention and is conducive to determining better follow-up treatments. In this paper, we propose a deep learning (DL) based dual-tasks network, named FaNet, that can perform rapid both diagnosis and severity assessments for COVID-19 based on the combination of 3D CT imaging and clinical symptoms. Generally, 3D CT image sequences provide more spatial information than do single CT images. In addition, the clinical symptoms can be considered as prior information to improve the assessment accuracy; these symptoms are typically quickly and easily accessible to radiologists. Therefore, we designed a network that considers both CT image information and existing clinical symptom information and conducted experiments on 416 patient data, including 207 normal chest CT cases and 209 COVID-19 confirmed ones. The experimental results demonstrate the effectiveness of the additional symptom prior information as well as the network architecture designing. The proposed FaNet achieved an accuracy of 98.28% on diagnosis assessment and 94.83% on severity assessment for test datasets. In the future, we will collect more covid-CT patient data and seek further improvement.

Clinical features and outcomes of seven patients with COVID-19 in a family cluster.

BACKGROUND: The family cluster is one of most important modes of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) transmission throughout China, and more details are needed about how family clusters cause the spread of coronavirus disease 2019 (COVID-19). CASE PRESENTATION: We retrospectively reviewed 7 confirmed cases from one family cluster. Both clinical features and laboratory examination results were described. Patient 1 had been in close contact with someone who was later confirmed to have COVID-19 in Wuhan City before he returned back to his hometown. He had dinner with 6 other members in his family. All the persons developed COVID-19 successively except for one older woman who neither had dinner with them nor shared a sleeping room with her husband. Six patients had mild or moderate COVID-19 but one older man with underlying diseases progressed into the severe type. After general and symptomatic treatments, all the patients recovered. CONCLUSIONS: In a family cluster, having dinner together may be an important mode for the transmission of SARS-CoV-2. In this setting, most cases are mild with a favorable prognosis, while elderly patients with underlying diseases may progress into the severe type. For someone who has close contact with a confirmed case, 14-day isolation is necessary to contain virus transmission.

A retrospective analysis of the epidemiology, clinical manifestations, and imaging characteristics of familial cluster-onset COVID-19.

BACKGROUND: The coronavirus disease 2019 (COVID-19) virus has a high incidence rate and strong infectivity. The diagnosis and evaluation of familial outbreaks requires a collective consideration of epidemiological history, molecular detection methods, chest computed tomography (CT), and clinical symptoms. METHODS: A group of family patients with COVID-19 diagnosed in Guizhou, China, in February 2020, was retrospectively analyzed. As of March 1, all patients in the group have been discharged from hospital. This study tracked all patients in the group. We report the epidemiology, radiological characteristics, treatment, and clinical outcomes of these patients. RESULTS: We collected a group of 8 clustered cases (3 men and 5 women) from a family with confirmed COVID-19 infection. In the first admission diagnosis, according to the degree of clinical symptoms, the 8 patients were defined as mild type (4/8) or moderate type (4/8). They were also divided according to the CT findings into early period (1/8), progressive period (3/8), and negative on CT scan (4/8); for the first 4 patients, the corresponding CT image scores were 1, 4, 5, and 5 respectively. In this group of COVID-19 patients, half of the patients showed occult clinical manifestations and negative CT performance. We defined these patients as COVID-19-infected patients, or asymptomatic carriers. CONCLUSIONS: The family cluster analysis indicated that COVID-19-infected patients (asymptomatic carriers) and symptomatic COVID-19 patients are distinct but coexistent. This may indicate that the infectivity and virulence of severe acute respiratory syndrome coronavirus 2 (SARS-COV-2) has decreased. In order to block the transmission pathway of this virus before it spreads, we need to identify the presence of asymptomatic carriers as early as possible.

Coronavirus disease 2019 (COVID-19): two case reports from a family cluster.

The coronavirus disease 2019 (COVID-19) is a new infectious disease, firstly appeared in Wuhan city and has rapidly spread to 114 countries outside China, which is receiving worldwide attention. As two important means of examination, computed tomography (CT) and real-time reverse transcription polymerase chain reaction (RT-PCR) have always been controversial in the clinical diagnosis of COVID-19 pneumonia. Here, we report a family cluster case of a father and a son diagnosed as COVID-19 at our hospital, and described the clinical manifestations, laboratory results, CT changes, diagnosis and treatment strategy of these two patients. Focus on the value of these two methods in the diagnosis and treatment of diseases, as well as their respective deficiencies. For patient 1 (father), the efficacy of RT-PCR is not satisfactory either in terms of diagnosis or follow-up, which may cause misdiagnosis and delay treatment. For patient 2 (son), the clinical symptoms were not obvious, but CT imaging clearly displayed dynamic changes of the lung lesions. Meanwhile, the two patients respectively underwent five chest CT examinations during their hospitalization and discharge follow-up, showing the potential harm of radiation. Therefore, in clinical work, doctors should make full use of the advantages of CT and RT-PCR, and take other measures to make up for their disadvantages.