Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Object:To explore the feasibility of using artificial intelligence for quality control of echocardiographic images.Methods:Retrospectively，5 000 two-dimensional echocardiographic video images within the period from 2021 to 2023 were randomly retrieved from the echocardiography database of Nanjing Drum Tower Hospital，Affiliated Hospital of Medical School，Nanjing University. Among these selected images，1 559 of them were apical views. The physician team formulated the scoring rules，which specifically included four scoring criteria：gain，scaling ratio，cardiac axis angle，and structure. Subsequently，the data were labeled with view classification and image quality scores. The labeled data were further partitioned into the training set（ n = 643），the validation set（ n = 276），and the test set（ n = 640）. The training and validation sets were utilized for constructing the models for view classification and quality assessment，while the test set was employed to verify the models' effectiveness. The view classification module was implemented using the SlowFast model，and the quality assessment module involved algorithms such as ResNet，Video Swin Transformer，SSD，and U-Net. Results:The average accuracy，precision，recall rate and F1 score of the classification model in identifying each apical view were 0.987 1，0.983 0，0.987 1 and 0.984 9 respectively，and the inference time was（333.4 ± 105.4）ms. The average accuracies of the quality assessment module in terms of gain，scaling ratio，cardiac axis angle and display of main structures were 0.915 1，0.928 2，0.938 7 and 0.965 6 respectively，and the overall scoring accuracy was 0.912 7.Conclusions:The echocardiogram quality control system developed in this research can effectively classify and evaluate the quality of two-dimensional images of the apical views in echocardiograms. Moreover，it guarantees the objectivity，timeliness and high-efficiency of quality control，which has reference value for the establishment of the echocardiogram quality control system.

Objective:To investigate perinatal outcomes of pregnant women with the resolution of placenta previa in the second trimester.Methods:This study was a retrospective cohort study, which included singleton pregnant women who received prenatal care and delivered at Peking University Third Hospital from January 1st 2019 to December 31st 2020. A total of 403 pregnant women diagnosed with placenta previa by ultrasound at 20-24 weeks of gestation and the placental position returned to normal before delivery were included in the study group, and 403 pregnant women with normal placental position matched 1∶1 were the control group. The primary outcome was postpartum hemorrhage rate, and secondary outcomes included postpartum bleeding volume, severe hemorrhage complications, blood transfusion, drug application, the application of instrument or surgical hemostasis measures, gestational week of delivery, and neonatal outcomes. The perinatal outcomes were analyzed by univariate and multivariate logistic regression methods.Results:(1) Compared with the control group, the incidence of postpartum hemorrhage [10.4% (42/403) vs 17.6% (71/403)], postpartum hemorrhage volume (median: 375 vs 400 ml), the proportion of postpartum hemorrhage≥500 ml [18.6% (75/403) vs 30.5% (123/403)], and the proportion of application of instrument or surgical hemostasis measures [1.7% (7/403) vs 4.5% (18/403)] in the study group were increased, and the differences were statistically significant (all P<0.05). Neonatal outcomes, including birth weight, small for gestational age, hospitalization in neonatal intensive care unit and incidence of neonatal asphyxia, were compared between the two groups, and there were no statistically significant differences (all P>0.05). (2) In pregnant women with vaginal delivery, the postpartum hemorrhage rate [31.7% (66/208) vs 17.5% (39/223)], postpartum hemorrhage volume (median: 390 vs 380 ml), the proportion of instrument or surgical hemostasis measures [3.8% (8/208) vs 0.4% (1/223)] of the study group were higher than those of the control group, and the differences were statistically significant (all P<0.05). There was no significant difference in the rate of postpartum hemorrhage between the study group and the control group who gave birth by cesarean setion ( P=0.545), but the proportion of postpartum hemorrhage≥500 ml in the study group and the control group were 29.2% (57/195) and 20.0% (36/180), and the difference was statistically significant ( P=0.039). (3) The results of multivariate analysis showed that compared with the control group, the risk of postpartum hemorrhage (a OR=2.042, 95% CI: 1.313-3.175), the application of drugs (a OR=1.684, 95% CI: 1.142-2.484) and the application of instruments or surgical hemostasis measures (a OR=2.696, 95% CI: 1.089-6.675) were significantly increased in the study group (all P<0.05). Among women who delivered vaginally, the risk of postpartum hemorrhage in the study group was 2.021 times greater than that in the control group (95% CI: 1.269-3.220; P=0.003). Conclusion:In women with placental previa in the second trimester of pregnancy, even if the placental position returns to normal before delivery, it is still a high risk factor for postpartum hemorrhage, especially in vaginal delivery.

Objective:To solve the ultimate problem of research on tongue's texture,that is the real appearance and development rules of tongue's texture on healthy person.This article is proposed to provide the references on feature recognition of creature and judgments on cases of tongue's texture.Methods:After taking the continuous,systematic and different ages samples of tongue's texture by three medical instruments and the multidirectional double smoothing dimension extraction algorithm of area of tongue's texture,the binary images of tongue's texture are extracted and stored in computer database of tongue's texture to analyze comprehensively and systematically.Results:Among the 5 535 healthy person,it can be divided into two groups,the number of one group which couldn't diagnose tongue's texture exactly with naked eyes(without any amplification)is about 1 074 accounted for 19.40%and the number of another group which could diagnose tongue's texture clearly with naked eyes and computers is about 4 464 accounted for 80.60%.There are 18 sorts according to the appearances of tongue's texture in which the number of straight tongue's texture is about 69.00%,the horizontal tongue's texture is about 18.54%, the oblique tongue's texture is about 11.48%and the other tongue's texture is about 0.99%.Conclusions:The tongue's texture of healthy person show appearances of regularity,straight,horizontal and oblique texture.From the development rules of tongue's texture on 30 continuous observing healthy person and among the 1 233 cases,these conclusions could be made that any kind of slightly imbalance of yin and yang or obstruction of entropy flow will lead to the changes of color,diaphragm,material being of tongue's texture.It will also change according to the patient's condition.

Objective To find out a method that is simple and reliable for screening phenylketonuria(PKU) heterzygotes.Methods Detecting and analyzing plasma phenylalanine(Phe) and tyrosine(Tyr) from 101 known-PKU heterozygotes and 2023 married people by means of a high performance liquid chromatograph.Results Significant differences were observed between known-PKU heterozygotes and screening population in plasma Phe, Phe/Tyr and Phe2/Tyr. The values of plasma Phe, Phe/Tyr and Phe2/Tyr from 94.1% known-PKU heterozygotes were higher than +2s of those values from screening population. According to the limits of +2s,81 cases were screened out from the 2023 married people. It was the first time to get the actual heterozygote frequency of 4% from population.Conculsion The biochemical method mentioned above is available for use in screening PKU heterozygotes.