Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening effi...Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening efficiency,accessibility,and cost-effectiveness.This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening.Methods:A systematic review of studies published before September 2024 was conducted throughout PubMed,Scopus,Embase,the Cochrane Library,the National Health Service Economic Evaluation Database,and the Cost-Effectiveness Analysis Registry.Eligible studies were included if they were(1)conducted among type 1 diabetes mellitus or type 2 diabetes mellitus adult diabetic population;(2)studies compared AI-based DR screening strategy to non-AI screening;and(3)performed a cost-effectiveness analysis.Meta-analysis was applied to pool incremental net benefit(INB)across studies stratified by country income and study perspective using a random-effects model.Statistical heterogeneity among studies was assessed using the I2 statistic,Cochrane Q statistics,and meta regression.Results:Nine studies were included in the analysis.From a healthcare system/payer perspective,AI-based DR screening was significantly cost-effective compared to non-AI-based screening,with a pooled INB of 615.77(95%confidence interval[CI]:558.27-673.27).Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries(INB=613.62,95%CI:556.06-671.18)and upper-/lower-middle income countries(INB=1,739.97,95%CI:423.13-3,056.82)with low heterogeneity.From a societal perspective,AI-based DR screening was generally cost-effective(INB=5,102.33,95%CI:-815.47-11,020.13),though the result lacked statistical significance and showed high heterogeneity.Conclusions:AI-based DR screening is generally cost-effective from a healthcare system perspective,particularly in high-income countries.Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations,to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.展开更多
The activity of Mo_(2) C-based catalyst on vegetable oil conversion into biofuel could be greatedly promoted by tuning the carbon content,while its modification mechanism on the surface properties remained elusive.Her...The activity of Mo_(2) C-based catalyst on vegetable oil conversion into biofuel could be greatedly promoted by tuning the carbon content,while its modification mechanism on the surface properties remained elusive.Herein,the exposed active sites,the particle size and Lewis acid amount of Ni-Mo_(2) C/MCM-41 catalysts were regulated by varying CH_(4) content in carbonization gas.The activity of Ni-Mo_(2) C/MCM-41 catalysts in jatropha oil(JO)conversion showed a volcano-like trend over the catalysts with increasing CH_(4) content from 15%to 50%in the preparation process.The one prepared by 25%CH_(4) content(NiMo_(2) C(25)/MCM-41)exhibited the outstanding catalytic performance with 83.9 wt%biofuel yield and95.2%C_(15)-C_(18) selectivity.Such a variation of activity was ascribed to the most exposed active sites,the smallest particle size,and the lowest Lewis acid amount from Ni^(0) on the Ni-Mo_(2) C(25)/MCM-41 catalyst surface.Moreover,the Ni-Mo_(2) C(25)/MCM-41 catalyst could also effectively catalyze the conversion of crude waste cooking oil(WCO)into green diesel.This study offers an effective strategy to improve catalytic performance of molybdenum carbide catalyst on vegetable oil conversion.展开更多
基金supported by the Global STEM Professorship Scheme(P0046113)Henry G.Leong Endowed Professorship in Elderly Vision Health.
文摘Objective:Diabetic retinopathy(DR)is a top leading cause of blindness worldwide,requiring early detection for timely intervention.Artificial intelligence(AI)has emerged as a promising tool to improve DR screening efficiency,accessibility,and cost-effectiveness.This study conducted a systematic review of literature and meta-analysis on the economic outcomes of AI-based DR screening.Methods:A systematic review of studies published before September 2024 was conducted throughout PubMed,Scopus,Embase,the Cochrane Library,the National Health Service Economic Evaluation Database,and the Cost-Effectiveness Analysis Registry.Eligible studies were included if they were(1)conducted among type 1 diabetes mellitus or type 2 diabetes mellitus adult diabetic population;(2)studies compared AI-based DR screening strategy to non-AI screening;and(3)performed a cost-effectiveness analysis.Meta-analysis was applied to pool incremental net benefit(INB)across studies stratified by country income and study perspective using a random-effects model.Statistical heterogeneity among studies was assessed using the I2 statistic,Cochrane Q statistics,and meta regression.Results:Nine studies were included in the analysis.From a healthcare system/payer perspective,AI-based DR screening was significantly cost-effective compared to non-AI-based screening,with a pooled INB of 615.77(95%confidence interval[CI]:558.27-673.27).Subgroup analysis showed robust cost-effectiveness of AI-based DR screening in high-income countries(INB=613.62,95%CI:556.06-671.18)and upper-/lower-middle income countries(INB=1,739.97,95%CI:423.13-3,056.82)with low heterogeneity.From a societal perspective,AI-based DR screening was generally cost-effective(INB=5,102.33,95%CI:-815.47-11,020.13),though the result lacked statistical significance and showed high heterogeneity.Conclusions:AI-based DR screening is generally cost-effective from a healthcare system perspective,particularly in high-income countries.Heterogeneity in cost-effectiveness across different perspectives highlights the importance of context-specific evaluations,to accurately evaluate the potential of AI-based DR screening in reducing global healthcare disparities.
基金financially supported by the National Natural Science Foundation of China(No.21972099)the National Natural Science Foundation of China(National Special Scientific Research Instrument and Equipment Development)(No.21427803-2)the 111 project(No.B17030)。
文摘The activity of Mo_(2) C-based catalyst on vegetable oil conversion into biofuel could be greatedly promoted by tuning the carbon content,while its modification mechanism on the surface properties remained elusive.Herein,the exposed active sites,the particle size and Lewis acid amount of Ni-Mo_(2) C/MCM-41 catalysts were regulated by varying CH_(4) content in carbonization gas.The activity of Ni-Mo_(2) C/MCM-41 catalysts in jatropha oil(JO)conversion showed a volcano-like trend over the catalysts with increasing CH_(4) content from 15%to 50%in the preparation process.The one prepared by 25%CH_(4) content(NiMo_(2) C(25)/MCM-41)exhibited the outstanding catalytic performance with 83.9 wt%biofuel yield and95.2%C_(15)-C_(18) selectivity.Such a variation of activity was ascribed to the most exposed active sites,the smallest particle size,and the lowest Lewis acid amount from Ni^(0) on the Ni-Mo_(2) C(25)/MCM-41 catalyst surface.Moreover,the Ni-Mo_(2) C(25)/MCM-41 catalyst could also effectively catalyze the conversion of crude waste cooking oil(WCO)into green diesel.This study offers an effective strategy to improve catalytic performance of molybdenum carbide catalyst on vegetable oil conversion.
