BMJ open ophthalmology · 2024
Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence.
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Luo Ruiyu, Wang Zhirong, Li Zhidong, Xie Rui, Zhang Yuan, Ye Guitong, Chen Jianqi, Xiao Yue, Zhan Jinan, Zhu Yingting, Zhuo Yehong
Аннотация
OBJECTIVES: This study aimed to quantitatively evaluate optic nerve head and retinal vascular parameters in children with hyperopia in relation to age and spherical equivalent refraction (SER) using artificial intelligence (AI)-based analysis of colour fundus photographs (CFP).
METHODS AND ANALYSIS: This cross-sectional study included 324 children with hyperopia aged 3-12 years. Participants were divided into low hyperopia (SER+0.5 D to+2.0 D) and moderate-to-high hyperopia (SER≥+2.0 D) groups. Fundus parameters, such as optic disc area and mean vessel diameter, were automatically and quantitatively detected using AI. Significant variables (p<0.05) in the univariate analysis were included in a stepwise multiple linear regression.
RESULTS: Overall, 324 children were included, 172 with low and 152 with moderate-to-high hyperopia. The median optic disc area and vessel diameter were 1.42 mm2 and 65.09 µm, respectively. Children with high hyperopia had larger superior neuroretinal rim (NRR) width and larger vessel diameter than those with low and moderate hyperopia. In the univariate analysis, axial length was significantly associated with smaller superior NRR width (β=-3.030, p<0.001), smaller temporal NRR width (β=-1.469, p=0.020) and smaller vessel diameter (β=-0.076, p<0.001). A mild inverse correlation was observed between the optic disc area and vertical disc diameter with age.
CONCLUSION: AI-based CFP analysis showed that children with high hyperopia had larger mean vessel diameter but smaller vertical cup-to-disc ratio than those with low hyperopia. This suggests that AI can provide quantitative data on fundus parameters in children with hyperopia.
METHODS AND ANALYSIS: This cross-sectional study included 324 children with hyperopia aged 3-12 years. Participants were divided into low hyperopia (SER+0.5 D to+2.0 D) and moderate-to-high hyperopia (SER≥+2.0 D) groups. Fundus parameters, such as optic disc area and mean vessel diameter, were automatically and quantitatively detected using AI. Significant variables (p<0.05) in the univariate analysis were included in a stepwise multiple linear regression.
RESULTS: Overall, 324 children were included, 172 with low and 152 with moderate-to-high hyperopia. The median optic disc area and vessel diameter were 1.42 mm2 and 65.09 µm, respectively. Children with high hyperopia had larger superior neuroretinal rim (NRR) width and larger vessel diameter than those with low and moderate hyperopia. In the univariate analysis, axial length was significantly associated with smaller superior NRR width (β=-3.030, p<0.001), smaller temporal NRR width (β=-1.469, p=0.020) and smaller vessel diameter (β=-0.076, p<0.001). A mild inverse correlation was observed between the optic disc area and vertical disc diameter with age.
CONCLUSION: AI-based CFP analysis showed that children with high hyperopia had larger mean vessel diameter but smaller vertical cup-to-disc ratio than those with low hyperopia. This suggests that AI can provide quantitative data on fundus parameters in children with hyperopia.