Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence
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 Th...
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| Format: | Article |
| Language: | English |
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BMJ Publishing Group
2024-04-01
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| Series: | BMJ Open Ophthalmology |
| Online Access: | https://bmjophth.bmj.com/content/9/1/e001520.full |
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| author | Yuan Zhang Yingting Zhu Zhirong Wang Yue Xiao Yehong Zhuo Zhidong Li Guitong Ye Jianqi Chen Ruiyu Luo Rui Xie Jinan Zhan |
| author_facet | Yuan Zhang Yingting Zhu Zhirong Wang Yue Xiao Yehong Zhuo Zhidong Li Guitong Ye Jianqi Chen Ruiyu Luo Rui Xie Jinan Zhan |
| author_sort | Yuan Zhang |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-f7c40b7c8d9e4e60a4c61381bed2877d |
| institution | Kabale University |
| issn | 2397-3269 |
| language | English |
| publishDate | 2024-04-01 |
| publisher | BMJ Publishing Group |
| record_format | Article |
| series | BMJ Open Ophthalmology |
| spelling | doaj-art-f7c40b7c8d9e4e60a4c61381bed2877d2025-02-06T08:55:09ZengBMJ Publishing GroupBMJ Open Ophthalmology2397-32692024-04-019110.1136/bmjophth-2023-001520Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligenceYuan Zhang0Yingting Zhu1Zhirong Wang2Yue Xiao3Yehong Zhuo4Zhidong Li5Guitong Ye6Jianqi Chen7Ruiyu Luo8Rui Xie9Jinan Zhan10Ophthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaDepartment of Emergency, Peking University Third Hospital, Beijing, ChinaOphthalmic Center State Key Laboratory of Ophthalmology, Sun Yat-Sen University Zhongshan, Guangzhou, Guangdong, ChinaObjectives 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.https://bmjophth.bmj.com/content/9/1/e001520.full |
| spellingShingle | Yuan Zhang Yingting Zhu Zhirong Wang Yue Xiao Yehong Zhuo Zhidong Li Guitong Ye Jianqi Chen Ruiyu Luo Rui Xie Jinan Zhan Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence BMJ Open Ophthalmology |
| title | Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| title_full | Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| title_fullStr | Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| title_full_unstemmed | Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| title_short | Quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| title_sort | quantitative assessment of colour fundus photography in hyperopia children based on artificial intelligence |
| url | https://bmjophth.bmj.com/content/9/1/e001520.full |
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