Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

Polypoidal choroidal vasculopathy(PCV)is one of the important subtypes of neovascular age-related macular degeneration(nARMD), which causes severe vision loss. It is necessary to distinguish PCV from other nARMD subtypes to guide the clinical treatment plans and predict disease outcomes. In recent years, artificial intelligence(AI)has been widely used in the diagnosis and research of ophthalmic diseases. By utilizing machine learning or deep learning combined with examination images in disease classification, lesion segmentation, and quantitative assessment, etc. This article reviews the recent applications of AI in the differential diagnosis of PCV through various examination images, the segmentation and quantification of biomarkers, as well as the prediction of genotype, response to anti-vascular endothelial growth factor(VEGF)therapy, and the short-term risk of vitreous hemorrhage. It summarizes the difficulties and challenges in clinical practice of AI and looks forward to the advantages and development trends of AI in PCV applications in the future. The article aims to provide more information for further research and application, thereby improving the diagnostic rate of PCV, optimizing treatment plans, and improving patients' visual prognosis.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Under the backdrop of smart health management technology development, this article reviews research advances in smart monitoring, assessment and intervention technologies for older people with mild cognitive impairment, including the types, typical applications and results of monitoring, assessment and intervention technologies.In addition, from the perspective of community-dwelling older adults' cognitive health management, a model for innovative management of community-dwelling older adults' cognitive function taking advantage of smart health management technologies is proposed, aiming to enhance the acceptance of smart health technologies among older people with cognitive impairment and to provide policy advice on developing friendly communities for older people with cognitive impairment.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.

Objective To quantitatively analyze Chinese Healthy China initiative policies and provide a basis for the formulation and improvement of relevant policies.Methods The Healthy China initiative policies released at the national and provincial levels from 2016 to 2023 was selected as the research object.A PMC index model containing 9 primary variables and 42 secondary variables was constructed using text mining methods to evaluate the quality of the Healthy China initiative policies.Results Using 32 selected Healthy China initiative policies as sample policies,the average PMC index is 7.81,and the policy quality is good or above,at a moderate depression level.The PMC index average values of policy nature,policy content,and policy evaluation are relatively high in the primary variables,and the shortcomings of policy incentives and effectiveness levels are more obvious.Conclusion The overall coverage of the Healthy China initiative policies is relatively comprehensive,but there are still problems such as weak effectiveness levels,insufficient use of diverse incentives and corresponding policy tools,and uneven regional development.In the future,improvements should be made in terms of enriching effectiveness levels,balancing the use of policy tools,and strengthening inter regional exchanges and learning.