The rapid diagnosis and surgical prediction of necrotizing enterocolitis (NEC) remain a challenge because its complex pathogenesis has not been completely elucidated, and no single medical examination is specific for diagnosing NEC. Artificial intelligence (AI) has proven the robustness of multivariate analysis and been widely used in the diagnosis of complex diseases in the past decade. In this paper, a new multimodal AI system including feature engineering, machine learning (ML), and deep learning (DL) was constructed based on abdominal radiographs (ARs) and clinical data. A total of 4,535 ARs from 1,823 suspected NEC patients were analyzed by transfer learning, and then medical images and clinical parameters from 827 suspected NEC patients were used to train, validate, and test the AI system. Our results demonstrated that the system was effective in diagnosing NEC. In addition, the clinical datasets obtained one week before surgery from 379 NEC patients were studied by the multimodal AI system, and the results showed that it was capable of predicting which NEC patients required surgery. We compared the results in external test sets with those made by clinicians and found that the diagnostic and surgical predictive ability of the AI system was equivalent to that of experienced clinicians. This multimodal AI system can help clinicians improve diagnostic efficiency, reduce the number of missed diagnoses, and facilitate early diagnosis and treatment to prevent disease progression or even death.
Different countries have adopted various control measures for the COVID-19 pandemic in different periods, and as the virus continues to mutate, the progression of the pandemic and preventive measures adopted have varied dynamically over time. Thus, quantitative analysis of the dynamic impact of different factors such as vaccination, mutant virus, social isolation, etc., on transmission and predicting pandemic progress has become a difficult task. To overcome the challenges above and enable governments to formulate reasonable countermeasures against the ongoing COVID-19 pandemic, we integrate several mathematical methods and propose a new adaptive multifactorial and geographically diverse epidemiological model based on a modified version of the classical susceptible-exposed-infectious-recovered (SEIR) model. Based on public datasets, a multi-center study was carried out considering 21 regions. First, a retrospective study was conducted to predict the number of infections over the next 30 days in 13 representative pandemic areas worldwide with an accuracy of 87.53%, confirming the robustness of the proposed model. Second, the impact of three scenarios on COVID-19 was quantified based on the scalability of the model: two different vaccination regimens were analyzed, and it was found that the number of infections would progressively decrease over time after vaccination; variant virus caused a 301.55% increase in infections in the United Kingdom; and 3-tier social lockdown in the United Kingdom reduced the infections by 47.01%. Third, we made short-term prospective predictions for the next 15 and 30 days for six countries with severe COVID-19 transmission and the predicted trend is accurate. This study is expected to inform public health responses. Code and data are publicly available at https://github.com/yuanyuanpei7/covid-19.
The dynamic transmission of asymptomatic and symptomatic COVID-19 infections is difficult to quantify because asymptomatic infections are not readily recognized or self-identified. To address this issue, we collected data on asymptomatic and symptomatic infections from four Chinese regions (Beijing, Dalian, Xinjiang, and Guangzhou). These data were considered reliable because the government had implemented large-scale multiple testing during the outbreak in the four regions. We modified the classical susceptible–exposure–infection–recovery model and combined it with mathematical tools to quantitatively analyze the number of infections caused by asymptomatic and symptomatic infections during dynamic transmission, respectively. The results indicated that the ratios of the total number of asymptomatic to symptomatic infections were 0.13:1, 0.48:1, 0.29:1, and 0.15:1, respectively, in the four regions. However, the ratio of the total number of infections caused by asymptomatic and symptomatic infections were 4.64:1, 6.21:1, 1.49:1, and 1.76:1, respectively. Furthermore, the present study describes the daily number of healthy people infected by symptomatic and asymptomatic transmission and the dynamic transmission process. Although there were fewer asymptomatic infections in the four aforementioned regions, their infectivity was found to be significantly higher, implying a greater need for timely screening and control of infections, particularly asymptomatic ones, to contain the spread of COVID-19.
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