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Aims Computer-assisted auscultation has become available to assist clinicians with physical examinations to detect congenital heart disease (CHD). However, its accuracy and effectiveness remain to be evaluated. This study seeks to evaluate the accuracy of auscultations of abnormal heart sounds of an artificial intelligence-assisted auscultation (AI-AA) platform we create. Methods and results Initially, 1397 patients with CHD were enrolled in the study. The samples of their heart sounds were recorded and uploaded to the platform using a digital stethoscope. By the platform, both remote auscultation by a team of experienced cardiologists from Shanghai Children’s Medical Center and automatic auscultation of the heart sound samples were conducted. Samples of 35 patients were deemed unsuitable for the analysis; therefore, the remaining samples from 1362 patients (mean age—2.4 ± 3.1 years and 46% female) were analysed. Sensitivity, specificity, and accuracy were calculated for remote auscultation compared to experts’ face-to-face auscultation and for artificial intelligence automatic auscultation compared to experts’ face-to-face auscultation. Kappa coefficients were measured. Compared to face-to-face auscultation, remote auscultation detected abnormal heart sound with 98% sensitivity, 91% specificity, 97% accuracy, and kappa coefficient 0.87. AI-AA demonstrated 97% sensitivity, 89% specificity, 96% accuracy, and kappa coefficient 0.84. Conclusions The remote auscultations and automatic auscultations, using the AI-AA platform, reported high auscultation accuracy in detecting abnormal heart sound and showed excellent concordance to experts’ face-to-face auscultation. Hence, the platform may provide a feasible way to screen and detect CHD.

BACKGROUND: The infection rate of Coronavirus Disease 2019 (COVID-19) in children was less than that in adults. However, the underlining reason is not well known. METHODS: Children with COVID-19 were recruited from two Children’s Hospitals in Wuhan and Shanghai in this case-control study. The associations of initial symptoms with age, vaccinations of Bacillus Calmette Guerin (BCG), and influenza and pathogens were determined by Chi-square t-test. RESULTS: We evaluated 248 confirmed cases, and 56 suspected cases with COVID-19. The median age was 6.82 years old, and 118 cases (38.82%) were girls. Furthermore, 30.26% of all patients were asymptomatic cases. The percentage of asymptomatic cases vaccinated with BCG was not significantly higher than that without BCG vaccination [86/280 (30.71%) vs. 6/13 (46.15%), P=0.203], and initial symptoms were not related with immunized influenza vaccine (P=0.267). Compared to parameters in pediatric patients with normal body temperatures, patients with fever had higher C reactive protein (CRP) (P

Leukemia is the most common malignancy affecting children. The morphologic analysis of bone marrow smears is an important initial step for diagnosis. Recent publications demonstrated that artificial intelligence is able to classify blood cells but a long way from clinical use. A total of 1,732 bone marrow images were used for the training of a convolutional neural network (CNN). New techniques of deep learning were integrated and an end-to-end leukemia diagnosis system was developed by using raw images without pre-processing. The system creatively imitated the workflow of a hematologist by detecting and excluding uncountable and crushed cells, then classifying and counting the remain cells to make a diagnosis. The performance of the CNN in classifying WBCs achieved an accuracy of 82.93%, precision of 86.07% and F1 score of 82.02%. And the performance in diagnosing acute lymphoid leukemia achieved an accuracy of 89%, sensitivity of 86% and specificity of 95%. The system also performs well at detecting the bone marrow metastasis of lymphoma and neuroblastoma, achieving an average accuracy of 82.93%. This is the first study which included a wider variety of cell types in leukemia diagnosis, and achieved a relatively high performance in real clinical scenarios.

In order to investigate the clinical features of pregnant women and their neonates with coronavirus disease 2019 (COVID-19) and the evidence of vertical transmission of COVID-19, we retrieved studies included in PubMed, Medline and Chinese databases from January 1, 2000 to October 25, 2020 using relevant terms, such as 'COVID-19', 'vertical transmission' et al. in 'Title/Abstract'. Case reports and case series were included according to the inclusion and exclusion criteria. We conducted literature screening and data extraction, and performed literature bias risk assessment. Total of 13 case series and 16 case reports were collected, including a total of 564 pregnant women with COVID-19 and their 555 neonates, of which 549 neonates received nucleic acid test for the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and 18 neonates was diagnosed with COVID-19. The positive rate is 3.28%. Amniotic fluid of one woman was tested positive for SARS-CoV-2. The majority of infected neonates were born under strict infection control and received isolation and artificial feeding. Up till now, there is no sufficient evidence to exclude the possibility of vertical transmission for COVID-19 based on the current available data.

Background: The aim of the present study was to explore the association of vaccinations and clinical manifestations in pediatric patients with Coronavirus Disease 2019 (COVID–19). Methods: Pediatric patients with COVID–19 were recruited from Wuhan Children’s Hospital and Shanghai Children’s Medical Center, including 248 laboratory–confirmed and 56 suspected cases from January 21 to March 12, 2020. Suspected and confirmed cases were diagnosed basing on the Novel Coronavirus 2019 Diagnosis and Treatment Protocol, seventh version. The associations of initial symptoms with age, vaccinations, pathogens, and cytokines were determined via Chi–square tests, Spearman correlations, and logistic regressions. Findings: We assessed 248 (81.58%) laboratory–confirmed cases and 56 (18.42%) suspected cases of COVID–19. The median age of all patients was 6.82 years (interquartile range: 2.08–10.20), and 186 cases (61.18%) were boys; furthermore, 30.26% of all patients were asymptomatic cases. The percentage of asymptomatic cases vaccinated with Bacillus Calmette Guerin (BCG) was not significantly higher than that without BCG vaccination (86/280 [30.71%] vs 6/13 [46.15%], P = 0.203), initial symptoms was not corrected with immunized influenza vaccine ( P = 0.267). Significant correlations were found between age and immunoglobulin (Ig) A (P < 0.001), IgE (P = 0.021), IgG (P < 0.001), complement C4 (P = 0.031), interleukin (IL)–10 (P = 0.003), and tumor necrosis factor (TNF)–α (P = 0.044). Compared to parameters in pediatric patients with normal body temperatures, patients with fever had higher high sensitivity C reactive protein (hs-CRP) (P < 0.001), IL–4 (P = 0.005), IL–10 (P = 0.038), TNF–α (P = 0.004), and TNF–γ (P = 0.036). Interpretation: Pediatric COVID–19 patients with BCG vaccinations exhibit similar clinical manifestations compared to those without BCG vaccinations, and the severity of symptoms in pediatric patients may be related to the maturity of immune function. Funding Statement: This study was partly supported by Top Medical Young Talents of Hubei Province, the National Science Foundation of China [81872637], Shanghai Municipal Commission of Health and Family Planning[201840324], Science and Technology Development Program of Pudong Shanghai New District [PKJ2017–Y01], Shanghai Professional and Technical Services Platform [18DZ2294100], and Shanghai Science and Technology Commission of Shanghai Municipality (17411965300, 17XD1402800,19441904400]. Declaration of Interests: The authors declare no competing interests. Ethics Approval Statement: This study was approved by the Institutional Review Board of Wuhan Children’s Hospital (IEC–2020R003–E01) and Shanghai Children’s Medical Center (SCMCIRB– K2020019–1).