Purpose: Artificial intelligence (AI) significantly enhances the screening and diagnostic processes for retinopathy of prematurity (ROP). In this article,we focused on the application and performance of AI in detecting ROP and distinguishing plus disease (PLUS) in ROP. Methods: We searched PubMed, Embase, Medline, Web of Science, and Ovid for studies published from January 2018 to July 2024. Studies evaluating the diagnostic performance of AI with expert ophthalmologists’judgment as a reference standard were included. The risk of bias was assessed using the QUADAS-2 tool and QUADAS-AI tool.Statistical analysis included data pooling, forest plot construction, heterogeneity testing, and meta-regression. Results: Fourteen of the 186 studies were included.The pooled sensitivity, specificity and the area under the curve (AUC) of the AI diagnosing ROP were 0.95 (95% CI 0.93-0.96), 0.97 (95% CI 0.94-0.98) and 0.97 (95% CI 0.95-0.98), respectively.The pooled sensitivity, specificity and the AUC of the AI distinguishing PLUS were 0.92 (95% CI 0.80-0.97),0.95 (95% CI 0.91-0.97) and 0.98 (95% CI 0.96-0.99), respectively.Cochran’s Q test (P < 0.01) andHiggins I ² heterogeneity index revealed considerable heterogeneity. The country of study, number of centers, data source and the number of doctors were responsible for the heterogeneity. For ROP diagnosing, researches conducted in China using private data in single center with less than 3 doctors showed higher sensitivity and specificity. For PLUS distinguishing, researches in multiple centers with less than 3 doctors showed higher sensitivity. Conclusions: This study revealed the powerful role of AI in diagnosing ROP and distinguishing PLUS. However, significant heterogeneity was noted among all included studies, indicating challenges in the application of AI for ROP diagnosis in real-world settings. More studies are needed to address these disparities, aiming to fully harness AI’s potential in augmenting medical care for ROP.

Purpose: Artificial intelligence (AI) significantly enhances the screening and diagnostic processes for retinopathy of prematurity (ROP). In this article,we focused on the application and performance of AI in detecting ROP and distinguishing plus disease (PLUS) in ROP. Methods: We searched PubMed, Embase, Medline, Web of Science, and Ovid for studies published from January 2018 to July 2024. Studies evaluating the diagnostic performance of AI with expert ophthalmologists’judgment as a reference standard were included. The risk of bias was assessed using the QUADAS-2 tool and QUADAS-AI tool.Statistical analysis included data pooling, forest plot construction, heterogeneity testing, and meta-regression. Results: Fourteen of the 186 studieswere included.The pooled sensitivity, specificity and the area under the curve (AUC) of the AI diagnosing ROP were 0.95 (95% CI 0.93-0.96), 0.97 (95% CI 0.94-0.98) and 0.97 (95% CI 0.95-0.98), respectively.The pooled sensitivity, specificity and the AUC of the AI distinguishing PLUS were 0.92 (95% CI 0.80-0.97),0.95 (95% CI 0.91-0.97) and 0.98 (95% CI 0.96-0.99), respectively.Cochran’s Q test (P < 0.01) andHiggins I ² heterogeneity index revealed considerable heterogeneity. The country of study, number of centers, data source and the number of doctors were responsible for the heterogeneity. For ROP diagnosing, researches conducted in China using private data in single center with less than 3 doctors showed higher sensitivity and specificity. For PLUS distinguishing, researches in multiple centers with less than 3 doctors showed higher sensitivity. Conclusions: This study revealed the powerful role of AI in diagnosing ROP and distinguishing PLUS. However, significant heterogeneity was noted among all included studies, indicating challenges in the application of AI for ROP diagnosis in real-world settings. More studies are needed to address these disparities, aiming to fully harness AI’s potential in augmenting medical care for ROP.

Background: 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 = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), 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.

Background: 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 = 1739.97, 95% CI: 423.13, 3056.82) with low heterogeneity. From a societal perspective, AI-based DR screening was generally cost-effective (INB= 5102.33, 95% CI: -815.47, 11020.13), 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.

Objective: To evaluate the effectiveness of music therapy on the anxiety level and physiological response of patients undergoing ophthalmic surgery.

Methods: Relevant randomized controlled trials that compared the combined effect of music therapy for patients undergoing ophthalmic surgery were included. Four English databases and three Chinese databases were searched from inception to Jan. 2022. Two reviewers independently performed data extraction and risk of bias assessments. The Cochrane Collaboration tool was used to assess the risk of bias. Meta-analysis was performed using Review Manager 5.3. The outcomes were overall anxiety, blood pressure, heart rate and pain.

Results: Atotal of 11 trials with 1,469 participants were included in the meta-analysis. Compared to standard care, music therapy had a good effect on reducing the anxiety levels of patients undergoing ophthalmic surgery (p<0.05). The results also suggested that music therapy  produced a signifcant improvement in blood pressure (p<0.05) and heart rate (p<0.05). The visual analogue scale (VAS) showed that music therapy signifcantly reduced pain compared to standard care (p<0.05).

Conclusions: This meta-analysis provided evidence that music therapy has an obvious effect on relieving anxiety levels, while it is also more effective in alleviating pain and improving physiological responses than standard care alone. Our fndings may provide accurate evidence-based guidance for the clinical implementation of music therapy. In the future, more high-quality studies are required for verifying these results. 

Objective: To evaluate the effectiveness of music therapy on the anxiety level and physiological response of patients undergoing ophthalmic surgery.

Methods: Relevant randomized controlled trials that compared the combined effect of music therapy for patients undergoing ophthalmic surgery were included. Four English databases and three Chinese databases were searched from inception to Jan. 2022. Two reviewers independently performed data extraction and risk of bias assessments. The Cochrane Collaboration tool was used to assess the risk of bias. Meta-analysis was performed using Review Manager 5.3. The outcomes were overall anxiety, blood pressure, heart rate and pain.

Results: Atotal of 11 trials with 1,469 participants were included in the meta-analysis. Compared to standard care, music therapy had a good effect on reducing the anxiety levels of patients undergoing ophthalmic surgery (p<0.05). The results also suggested that music therapy  produced a signifcant improvement in blood pressure (p<0.05) and heart rate (p<0.05). The visual analogue scale (VAS) showed that music therapy signifcantly reduced pain compared to standard care (p<0.05).

Conclusions: This meta-analysis provided evidence that music therapy has an obvious efect on relieving anxiety levels, while it is also more effective in alleviating pain and improving physiological responses than standard care alone. Our fndings may provide accurate evidence-based guidance for the clinical implementation of music therapy. In the future, more high-quality studies are required for verifying these results.