An Insight of the First Community Infected COVID-19 Patient in Beijing by Imported Case: Role of Deep Learning-Assisted CT Diagnosis

In the era of coronavirus disease 2019(COVID-19)pandemic,imported COVID-19 cases pose great challenges to many countries.Chest CT examination is considered to be complementary to nucleic acid test for COVID-19 detection and diagnosis.Wie report the first community infected COVID-19 patient by an imported case in Beijing,which manifested as nodular lesions on chest CT imaging at the early stage.Deep Learning(DL)-based diagnostic systems quantitatively monitored the progress of pulmonary lesions in 6 days and timely made alert for suspected pneumonia,so that prompt medical isolation was taken.The patient was confirmed as COVID-19 case after nucleic acid test,for which the community transmission was prevented timely.The roles of DL-assisted diagnosis in helping radiologists screening suspected COVID cases were discussed.

A machine learning model for diagnosing acute pulmonary embolism and comparison with Wells score,revised Geneva score,and Years algorithm

Background:Acute pulmonary embolism(APE)is a fatal cardiovascular disease,yet missed diagnosis and misdiagnosis often occur due to non-specific symptoms and signs.A simple,objective technique will help clinicians make a quick and precise diagnosis.In population studies,machine learning(ML)plays a critical role in characterizing cardiovascular risks,predicting outcomes,and identifying biomarkers.This work sought to develop an ML model for helping APE diagnosis and compare it against current clinical probability assessment models.Methods:This is a single-center retrospective study.Patients with suspected APE were continuously enrolled and randomly divided into two groups including training and testing sets.A total of 8 ML models,including random forest(RF),Naïve Bayes,decision tree,K-nearest neighbors,logistic regression,multi-layer perceptron,support vector machine,and gradient boosting decision tree were developed based on the training set to diagnose APE.Thereafter,the model with the best diagnostic performance was selected and evaluated against the current clinical assessment strategies,including the Wells score,revised Geneva score,and Years algorithm.Eventually,the ML model was internally validated to assess the diagnostic performance using receiver operating characteristic(ROC)analysis.Results:The ML models were constructed using eight clinical features,including D-dimer,cardiac troponin T(cTNT),arterial oxygen saturation,heart rate,chest pain,lower limb pain,hemoptysis,and chronic heart failure.Among eight ML models,the RF model achieved the best performance with the highest area under the curve(AUC)(AUC=0.774).Compared to the current clinical assessment strategies,the RF model outperformed the Wells score(P=0.030)and was not inferior to any other clinical probability assessment strategy.The AUC of the RF model for diagnosing APE onset in internal validation set was 0.726.Conclusions:Based on RF algorithm,a novel prediction model was finally constructed for APE diagnosis.When compared to the current clinical assessment strategies,the RF model achieved better diagnostic efficacy and accuracy.Therefore,the ML algorithm can be a useful tool in assisting with the diagnosis of APE.

Prevalence and risk factors of stroke in China:a national serial cross-sectional study from 2003 to 2018

Stroke imposes a substantial burden worldwide.With the rapid economic and lifestyle transition in China,trends of the prevalence of stroke across different geographic regions in China remain largely unknown.Capitalizing on the data in the National Health Services Surveys(NHSS),we assessed the prevalence and risk factors of stroke in China from 2003 to 2018.In this study,data from 2003,2008,2013,and 2018 NHSS were collected.Stroke cases were based on participants’self-report of a previous diagnosis by clinicians.We estimated the trends of stroke prevalence for the overall population and subgroups by age,sex,and socioeconomic factors,then compared across different geographic regions.We applied multivariable logistic regression to assess associations between stroke and risk factors.The number of participants aged 15 years or older were 154,077,146,231,230,067,and 212,318 in 2003,2008,2013,and 2018,respectively,among whom,1435,1996,3781,and 6069 were stroke patients.The age and sex standardized prevalence per 100,000 individuals was 879 in 2003,1100 in 2008,1098 in 2013,and 1613 in 2018.Prevalence per 100,000 individuals in rural areas increased from 669 in 2003 to 1898 in 2018,while urban areas had a stable trend from 1261 in 2003 to 1365 in 2018.Across geographic regions,the central region consistently had the highest prevalence,but the western region has an alarmingly increasing trend from 623/100,000 in 2003 to 1898/100,000 in 2018(Ptrend<0.001),surpassing the eastern region in 2013.Advanced age,male sex,rural area,central region,hypertension,diabetes,depression,low education and income level,retirement or unemployment,excessive physical activity,and unimproved sanitation facilities were significantly associated with stroke.In conclusion,the increasing prevalence of stroke in China was primarily driven by economically underdeveloped regions.It is important to develop targeted prevention programs in underdeveloped regions.Besides traditional risk factors,more attention should be paid to nontraditional risk factors to improve the prevention of stroke.

Survey on deep learning for pulmonary medical imaging

As a promising method in artificial intelligence,deep learning has been proven successful in several domains ranging from acoustics and images to natural language processing.With medical imaging becoming an important part of disease screening and diagnosis,deep learning-based approaches have emerged as powerful techniques in medical image areas.In this process,feature representations are learned directly and automatically from data,leading to remarkable breakthroughs in the medical field.Deep learning has been widely applied in medical imaging for improved image analysis.This paper reviews the major deep learning techniques in this time of rapid evolution and summarizes some of its key contributions and state-of-the-art outcomes.The topics include classification,detection,and segmentation tasks on medical image analysis with respect to pulmonary medical images,datasets,and benchmarks.A comprehensive overview of these methods implemented on various lung diseases consisting of pulmonary nodule diseases,pulmonary embolism,pneumonia,and interstitial lung disease is also provided.Lastly,the application of deep learning techniques to the medical image and an analysis of their future challenges and potential directions are discussed.

Machine learning models for outcome prediction of Chinese uveal melanoma patients: A 15-year follow-up study

Dear Editor Uveal melanoma(UM)is the most frequent primary malig-nant intraocular tumor in adults with an estimated inci-dence of 4-5 per million per year in western countries[1].About 50%of UM patients eventually develop metas-tasis.In a previous study,the prognosis of Chinese UM was mainly correlated with visual clinical features and gene sequencing results.Models designed to predict UM prognosis have been previously described[2],but these studies were based on Caucasians,not Chinese.