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 identify and analyze the bioinformatics and expression patterns of the F-box-LRR(FBXL)gene family of Salvia miltiorrhiza based on genomic data,and provide a foundation for further elucidating its gene functions.Methods The SmFBXL gene was identified from the Salvia miltiorrhiza genomic database.Its gene structure features,promoter cis-acting elements,physicochemical properties of encoded proteins,evolutionary relationships,and tissue expression were analyzed by bioinformatics methods and online tools.Results A total of 104 SmFBXL genes were identified from the Salvia miltiorrhiza genome,unevenly distributed on 8 chromosomes,with upstream promoters containing cis-acting elements related to plant stress resistance,growth and development,and hormone response.A phylogenetic tree of the FBXL family members of Salvia miltiorrhiza,Arabidopsis thaliana,and Glycine max was constructed,dividing the 104 SmFBXL genes into 7 subfamilies.Through homologous evolution analysis,it was speculated that SmFBXL36 might be involved in defense against pathogen invasion,SmFBXL86 and SmFBXL79 might play important roles in regulating lateral root growth in Salvia miltiorrhiza,and SmFBXL11 and SmFBXL40 might regulate hypocotyl growth.Transcriptome data showed differential expression of SmFBXL genes in different tissues of Salvia miltiorrhiza,with 13 SmFBXL genes showing higher expression levels in roots and leaves,serving as candidate genes for further research on the SmFBXL gene family.Conclusion The research results provided a reference for further elucidating the regulatory mechanisms of SmFBXL genes in stress response and secondary metabolite biosynthesis in Salvia miltiorrhiza.