目的:探讨国内外医疗质量研究热点及发展前沿,对比国内外医疗质量发展异同点,为推动医疗质量研究的深入发展提供科学依据和新视角。方法:以CNKI和Web of Science数据库中2013-2023年收录的卫生领域医疗质量相关文献为数据基础,采用文献...目的:探讨国内外医疗质量研究热点及发展前沿,对比国内外医疗质量发展异同点,为推动医疗质量研究的深入发展提供科学依据和新视角。方法:以CNKI和Web of Science数据库中2013-2023年收录的卫生领域医疗质量相关文献为数据基础,采用文献计量学和知识图谱方法,从论文数量、高产作者和研究机构分布、研究热点前沿及发展趋势等方面进行定量定性分析。结果:通过国内外医疗质量的文献可视化对比分析发现,国内外医疗质量发文量差距较大,且主要研究机构不同,医疗质量的研究热点和前沿也有所差异。结论:提高对医疗质量相关研究的支持力度,扩大研究的影响力,搭建医疗质量研究网络,鼓励多机构多作者协作,提升发文质量,推动公立医院高质量发展。展开更多
Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE vid...Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.展开更多
文摘目的:探讨国内外医疗质量研究热点及发展前沿,对比国内外医疗质量发展异同点,为推动医疗质量研究的深入发展提供科学依据和新视角。方法:以CNKI和Web of Science数据库中2013-2023年收录的卫生领域医疗质量相关文献为数据基础,采用文献计量学和知识图谱方法,从论文数量、高产作者和研究机构分布、研究热点前沿及发展趋势等方面进行定量定性分析。结果:通过国内外医疗质量的文献可视化对比分析发现,国内外医疗质量发文量差距较大,且主要研究机构不同,医疗质量的研究热点和前沿也有所差异。结论:提高对医疗质量相关研究的支持力度,扩大研究的影响力,搭建医疗质量研究网络,鼓励多机构多作者协作,提升发文质量,推动公立医院高质量发展。
文摘Objective To explore the value of deep learning(DL)models semi-automatic training system for automatic optimization of clinical image quality control of transthoracic echocardiography(TTE).Methods Totally 1250 TTE videos from 402 patients were retrospectively collected,including 490 apical four chamber(A4C),310 parasternal long axis view of left ventricle(PLAX)and 450 parasternal short axis view of great vessel(PSAX GV).The videos were divided into development set(245 A4C,155 PLAX,225 PSAX GV),semi-automated training set(98 A4C,62 PLAX,90 PSAX GV)and test set(147 A4C,93 PLAX,135 PSAX GV)at the ratio of 5∶2∶3.Based on development set and semi-automatic training set,DL model of quality control was semi-automatically iteratively optimized,and a semi-automatic training system was constructed,then the efficacy of DL models for recognizing TTE views and assessing imaging quality of TTE were verified in test set.Results After optimization,the overall accuracy,precision,recall,and F1 score of DL models for recognizing TTE views in test set improved from 97.33%,97.26%,97.26%and 97.26%to 99.73%,99.65%,99.77%and 99.71%,respectively,while the overall accuracy for assessing A4C,PLAX and PSAX GV TTE as standard views in test set improved from 89.12%,83.87%and 90.37%to 93.20%,90.32%and 93.33%,respectively.Conclusion The developed DL models semi-automatic training system could improve the efficiency of clinical imaging quality control of TTE and increase iteration speed.