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.

目的:研究形觉剥夺性和光学离焦性近视豚鼠视紫红质的表达变化,探讨视紫红质表达与实验性近视眼之间的关系。

方法:40只出生后1周的豚鼠随机分为形觉剥夺组和光学离焦组(n=20)，形觉剥夺组单眼戴半透明(半透明薄膜贴于平镜表面)平光硬性角膜接触镜片(Rigidgass-pemmeable contactlens, RCP)，光学离焦组单眼藏-4.0DRGP镜片，另一只眼为对照组。戴镜干预后1、2周各组分别测量屈光度、眼轴长度、玻璃体腔深度，并于上午10~12 点钟取材，实时荧光定量PCR观察视紫红质 mRNA 的变化 Westem-blot 观察视紫红质的变化，进行对比比较,统计分析。

结果:实验干预后1周，形觉剥夺组和光学离焦组与对照组相比各项指标无显著性差(除形觉剥夺组屈光度以外)。实验干预后2周，与对照组相比较，形觉剥夺组和光学离焦组明显发生近视、眼轴延长、玻璃体腔加深(t=22.20、18.32、19.65、15.78、6.18、11.20.P<0.01)：形觉剥夺组视紫红质及其 mRNA 表达均增加(t=17,489、14.31.P<0.05)光学离焦组视紫红质及其mRNA表达无明显变化。

结论:视紫红质的表达可能参与了形觉剥夺性近视眼的形成,而在光学离焦性近视眼中作用有限。

Purpose:To investigate the rhodopsin expression in form-deprived and defocus myopiain guinea pig and study the relationship between the rhodopsin expression andexperimental myopia.

Methods:Fourty guinea pigs were randomized into the form-deprived group and thedefocus group (n= 20 ). Guinea pigs in the form-deprived group wore a diffuser(rigidgass-permeable contact lens(RGP)on one eye since one week after birth. Those in defocus group wore a -4 D RGPon one eye. The contralateral eyes were left ascontrol. Refraction, axial length and depth of vitreous cavity were measured after 1and 2 weeks respectively. Retina were dissected at 10 ~ 12 o'clock in the moring.The level of rhodopsin and its mRNA were observed through Western-blot and real-time PCR respectively.

Result:There is no difference between form-deprived group, defocus group and controlgroups(except refraction in form-deprived group). One week later, there is nodifference between the form-deprived group, the defocus group and the control groups(except refraction in form-deprived group). Two weeks later, eyes in the form-deprivedgroup and the defocus group became myopic. Its axial length lengthened and depth ofvitreous cavity appeared deep. The form-deprived groups showed an increasedexpression of rhodopsin and its mRNA compared to the control groups. There is nodifference between the defocus group and the control groups.

Conclusion : Expression of rhodopsin might involve formation of form-deprived myopia,but has less influence on defocus myopia.