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1.
Hong Kong Journal of Paediatrics ; 27(2):118-125, 2022.
Article in English | Scopus | ID: covidwho-1843202

ABSTRACT

Since the first report of COVID-19 in Wuhan, China, the disease has rapidly spread to many countries worldwide. The initial reports showed that the incidence rate in adults was higher, while children and adolescents had fewer cases of infection. However, the number of COVID-19 cases has gradually increased in children and adolescents. Therefore, this study aimed to assess the percentage of children and/or adolescents of the total patients diagnosed with COVID-19. PubMed, Embase, Web of Science and the Cochrane Library were searched to find relevant studies. All statistical analyses were conducted using StataMP 14 software. A total of 12 studies met the inclusion criteria. The final results showed that the percentage of children and/or adolescents of all COVID-19 cases was 0.06 [95% confidence interval (CI), 0.04-0.07], which meant an average of 6 cases in children per 10,000 COVID-19 cases. The percentage of children and/or adolescents with COVID-19 was 0.03 (95% CI, 0.01-0.05), 0.09 (95% CI, 0.08-0.09), 0.09 (95% CI, 0.03-0.16) and 0.04 (95% CI, 0.00-0.10) in Asia, South America, North America and Europe, respectively. The present study showed a low percentage of COVID-19 cases of children and/or adolescents, but not without infection risk. Therefore, we should pay attention to the cases of children and/or adolescents during the COVID-19 period and raise our vigilance. © 2022, Medcom Limited. All rights reserved.

3.
Nature Machine Intelligence ; 2022.
Article in English | Scopus | ID: covidwho-1805663

ABSTRACT

In the version of this article initially published, the first name of Chuansheng Zheng was misspelled as Chuangsheng. The error has been corrected in the HTML and PDF versions of the article. © The Author(s) 2022.

4.
Nature Machine Intelligence ; 3(12):1081-1089, 2021.
Article in English | Web of Science | ID: covidwho-1585763

ABSTRACT

Artificial intelligence provides a promising solution for streamlining COVID-19 diagnoses;however, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalized model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the artificial intelligence (AI) model can be distributedly trained and independently executed at each host institution under a federated learning framework without data sharing. Here we show that our federated learning framework model considerably outperformed all of the local models (with a test sensitivity/specificity of 0.973/0.951 in China and 0.730/0.942 in the United Kingdom), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals without the federated learning framework) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans from 3,336 patients collected from 23 hospitals located in China and the United Kingdom. Collectively, our work advanced the prospects of utilizing federated learning for privacy-preserving AI in digital health. The COVID-19 pandemic sparked the need for international collaboration in using clinical data for rapid development of diagnosis and treatment methods. But the sensitive nature of medical data requires special care and ideally potentially sensitive data would not leave the organization which collected it. Xiang Bai and colleagues present a privacy-preserving AI framework for CT-based COVID-19 diagnosis and demonstrate it on data from 23 hospitals in China and the United Kingdom.

5.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-293214

ABSTRACT

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

6.
PUBMED; 2021.
Preprint in English | PUBMED | ID: ppcovidwho-292843

ABSTRACT

Artificial intelligence (AI) provides a promising substitution for streamlining COVID-19 diagnoses. However, concerns surrounding security and trustworthiness impede the collection of large-scale representative medical data, posing a considerable challenge for training a well-generalised model in clinical practices. To address this, we launch the Unified CT-COVID AI Diagnostic Initiative (UCADI), where the AI model can be distributedly trained and independently executed at each host institution under a federated learning framework (FL) without data sharing. Here we show that our FL model outperformed all the local models by a large yield (test sensitivity /specificity in China: 0.973/0.951, in the UK: 0.730/0.942), achieving comparable performance with a panel of professional radiologists. We further evaluated the model on the hold-out (collected from another two hospitals leaving out the FL) and heterogeneous (acquired with contrast materials) data, provided visual explanations for decisions made by the model, and analysed the trade-offs between the model performance and the communication costs in the federated training process. Our study is based on 9,573 chest computed tomography scans (CTs) from 3,336 patients collected from 23 hospitals located in China and the UK. Collectively, our work advanced the prospects of utilising federated learning for privacy-preserving AI in digital health.

7.
Eur Rev Med Pharmacol Sci ; 24(15): 8185-8186, 2020 08.
Article in English | MEDLINE | ID: covidwho-695438

ABSTRACT

OBJECTIVE: The World Health Organization reported a cluster of cases of pneumonia of unknown cause detected on December 31, 2019 in China. Unfortunately, a 34-year-old Italian nurse has committed suicide after testing positive for coronavirus. It was the second case of suicide by a nurse in an Italian hospital and occurred only a few days after the first suicide. These consecutive suicides have aroused concern, and it is necessary to investigate the psychological issues of the medical staff in Italy regarding the COVID-19.


Subject(s)
Coronavirus Infections/therapy , Health Personnel/psychology , Pneumonia, Viral/therapy , Suicide/psychology , Betacoronavirus , COVID-19 , Humans , Italy , Nurses , Pandemics , SARS-CoV-2 , Suicide/prevention & control
9.
Eur Rev Med Pharmacol Sci ; 24(9): 5176-5177, 2020 05.
Article in English | MEDLINE | ID: covidwho-333024

ABSTRACT

OBJECTIVE: COVID-19 broke out in China at the end of 2019 and spread rapidly around the world. The World Health Organization designated COVID-19 as a global pandemic on March 11, 2020. China has adopted its own country-specific comprehensive prevention and control measures, and, as a result, the domestic COVID-19 epidemic became effectively controlled in China in mid-March 2020. During the COVID-19 epidemic, remarkable changes have taken place in China's domestic learning, living, and working methods, primarily in terms of the synergy between the Internet Plus (Internet+) strategy and the leadership of the Chinese government.


Subject(s)
Coronavirus Infections/epidemiology , Internet , Pneumonia, Viral/epidemiology , Betacoronavirus , COVID-19 , China , Commerce , Coronavirus Infections/prevention & control , Education, Distance , Federal Government , Humans , Leadership , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , SARS-CoV-2
13.
Eur Rev Med Pharmacol Sci ; 24(6): 3397-3399, 2020 03.
Article in English | MEDLINE | ID: covidwho-48626

ABSTRACT

On December 31, 2019, the World Health Organization (WHO) reported a cluster of cases of pneumonia of unknown cause detected in Wuhan City, Hubei Province, China. As of February 29, 2020, the National Health Commission of China has reported 79,389 confirmed cases of SARS-CoV-2 infection in 34 provinces. The masks can be used to block respiratory transmission from human to human, and are an effective way to control influenza. It is, therefore, necessary to wear a mask when respiratory infectious diseases are prevalent. China has a population of 1.4 billion. Assuming that two-thirds of the people in China must wear a mask every day, the daily demand for masks will reach 900 million. The Chinese government has taken many measures to solve these problems. Additionally, more measures should be taken to properly dispose of mask garbage. Although the outbreak originated in China, person-to-person transmission of SARS-CoV-2 has been confirmed, which means that it can be spread to anywhere in the world if prevention measures fail. The issues regarding face mask shortages and garbage in China, therefore, deserve worldwide attention.


Subject(s)
Betacoronavirus , Coronavirus Infections/prevention & control , Disease Outbreaks , Masks/supply & distribution , Pandemics/prevention & control , Pneumonia, Viral/prevention & control , COVID-19 , Coronavirus Infections/epidemiology , Coronavirus Infections/transmission , Humans , Pneumonia, Viral/epidemiology , Pneumonia, Viral/transmission , SARS-CoV-2
14.
Public Health ; 183: 4-5, 2020 06.
Article in English | MEDLINE | ID: covidwho-38293
15.
Zhonghua Xin Xue Guan Bing Za Zhi ; 48(6): 456-460, 2020 Jun 24.
Article in Chinese | MEDLINE | ID: covidwho-8400

ABSTRACT

Objective: To analyze the clinical characteristics of the severe or critically ill patients with novel coronavirus pneumonia (COVID-19), and evaluate the impact of complicated myocardial injury on the prognosis of these patients. Methods: A retrospective study was conducted in 54 patients who admitted to Tongji hospital from February 3, 2020 to February 24, 2020 and met the criteria of severe or critical conditions of COVID-19. The clinical characteristics and hospital mortality rate were analyzed and compared between the patients with or without myocardial injury, which was defined with 3 times higher serum cardiac troponin value. Results: The age of the 54 patients was 68.0(59.8, 74.3) years. Among all the patients, 24 (44.4%) patients were complicated with hypertension, 13 (24.1%) with diabetes, 8 (14.8%) with coronary heart disease, and 3 (5.6%) with previous cerebral infarction. During hospitalization, 24 (44.4%) of the patients were complicated with myocardial injury and 26 (48.1%) patients died in hospital. In-hospital mortality was significantly higher in patients with myocardial injury than in patients without myocardial injury (14 (60.9%) vs. 8 (25.8%), P=0.013). Moreover, the levels of C-reactive protein (153.6 (80.3, 240.7) ng/L vs. 49.8 (15.9, 101.9) ng/L) and N-terminal pro-B-type natriuretic peptide (852.0 (400.0, 2 315.3) ng/L vs. 197.0 (115.3, 631.0) ng/L) were significantly higher than patients without myocardial injury (all P<0.01). Conclusions: Prevalence of myocardial injury is high among severe or critically ill COVID-19 patients. Severe or critically ill COVID-19 patients with myocardial injury face a significantly higher risk of in-hospital mortality. The study suggests that it is important to monitor and manage the myocardial injury during hospitalization for severe or critically ill COVID-19 patients.


Subject(s)
Betacoronavirus , Coronavirus Infections , Critical Illness , Heart Injuries , Pandemics , Pneumonia, Viral , Aged , COVID-19 , Coronavirus Infections/complications , Humans , Middle Aged , Pneumonia, Viral/complications , Retrospective Studies , SARS-CoV-2
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