人工智能定量分析不同近视程度儿童眼底豹纹状改变的差异性OACSTPCD

Objective Quantitative analysis of tessellated fundus by artificial intelligence techniques to compare the variability of tessellated fundus changes in children with different levels of myopia.Methods Optometry results,axial length,corneal curvature and fundus photographs of 327 right eyes of 327 cases of children aged 6～13 years attending our ophthalmology outpatient clinic from January 2023 to October 2023 were collected and categorized into 76 cases of normal control,86 cases of mild myopia,85 cases of moderate myopia and 80 cases of high myopia.Based on artificial intelli-gence techniques to quantify the myopic tessellated fundus pattern,the average choroidal exposed area per unit area of the fundus was calculated to obtain the fundus tessellated density(FTD),and the variability of the FTD was compared between the groups.Results Compared with the normal control group,the mild myopia group and the moderate myopia group,the total leopard spot density,the leopard spot density parameters of the 3mm diameter area centered on the macula above the nose,under the nose,supratemporal,infratemporal and the 6mm diameter area centered on the macula were statistically significant(P＜0.05).The subnasal region with a diameter of 6mm centered on macula was also significantly different between the moderate myopia group and the normal control group(P＜0.05).Conclusions The subnasal region,centered on the macula,is the initial area of the fundus to be affected by myopia and the growth of the eye axis.While tessellated fundus changes are not apparent in the early stages of myopia,the quantitative measurement of fundus tessellated density index,aided by the use of artificial intelligence,can assist in the detection of subtle changes in the tessellated fundus,thereby facilitating a more timely,comprehensive,and accurate monitoring of the progression of myopia.