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高度近视眼中视网膜血管的形态学特征:使用迁移学习系统通过人工智能分析的超广角图像。

Morphological characteristics of retinal vessels in eyes with high myopia: Ultra-wide field images analyzed by artificial intelligence using a transfer learning system.

作者信息

Mao Jianbo, Deng Xinyi, Ye Yu, Liu Hui, Fang Yuyan, Zhang Zhengxi, Chen Nuo, Sun Mingzhai, Shen Lijun

机构信息

Department of Ophthalmology, Center for Rehabilitation Medicine, Zhejiang Provincial People's Hospital (Affiliated People's Hospital, Hangzhou Medical College), Hangzhou, Zhejiang, China.

Eye Hospital of Wenzhou Medical University, Wenzhou, Zhejiang, China.

出版信息

Front Med (Lausanne). 2023 Feb 16;9:956179. doi: 10.3389/fmed.2022.956179. eCollection 2022.

Abstract

PURPOSE

The purpose of this study is to investigate the retinal vascular morphological characteristics in high myopia patients of different severity.

METHODS

317 eyes of high myopia patients and 104 eyes of healthy control subjects were included in this study. The severity of high myopia patients is classified into C0-C4 according to the Meta Analysis of the Pathologic Myopia (META-PM) classification and their vascular morphological characteristics in ultra-wide field imaging were analyzed using transfer learning methods and RU-net. Correlation with axial length (AL), best corrected visual acuity (BCVA) and age was analyzed. In addition, the vascular morphological characteristics of myopic choroidal neovascularization (mCNV) patients and their matched high myopia patients were compared.

RESULTS

The RU-net and transfer learning system of blood vessel segmentation had an accuracy of 98.24%, a sensitivity of 71.42%, a specificity of 99.37%, a precision of 73.68% and a F1 score of 72.29. Compared with healthy control group, high myopia group had smaller vessel angle (31.12 ± 2.27 vs. 32.33 ± 2.14), smaller fractal dimension (Df) (1.383 ± 0.060 vs. 1.424 ± 0.038), smaller vessel density (2.57 ± 0.96 vs. 3.92 ± 0.93) and fewer vascular branches (201.87 ± 75.92 vs. 271.31 ± 67.37), all < 0.001. With the increase of myopia maculopathy severity, vessel angle, Df, vessel density and vascular branches significantly decreased (all < 0.001). There were significant correlations of these characteristics with AL, BCVA and age. Patients with mCNV tended to have larger vessel density ( < 0.001) and more vascular branches ( = 0.045).

CONCLUSION

The RU-net and transfer learning technology used in this study has an accuracy of 98.24%, thus has good performance in quantitative analysis of vascular morphological characteristics in Ultra-wide field images. Along with the increase of myopic maculopathy severity and the elongation of eyeball, vessel angle, Df, vessel density and vascular branches decreased. Myopic CNV patients have larger vessel density and more vascular branches.

摘要

目的

本研究旨在调查不同严重程度的高度近视患者的视网膜血管形态特征。

方法

本研究纳入了317只高度近视患者的眼睛和104只健康对照者的眼睛。根据病理性近视的荟萃分析(META-PM)分类,将高度近视患者的严重程度分为C0-C4,并使用迁移学习方法和RU-net分析他们在超广角成像中的血管形态特征。分析其与眼轴长度(AL)、最佳矫正视力(BCVA)和年龄的相关性。此外,比较了近视性脉络膜新生血管(mCNV)患者及其匹配的高度近视患者的血管形态特征。

结果

血管分割的RU-net和迁移学习系统的准确率为98.24%,灵敏度为71.42%,特异性为99.37%,精确率为73.68%,F1评分为72.29。与健康对照组相比,高度近视组的血管角度较小(31.12±2.27对32.33±2.14),分形维数(Df)较小(1.383±0.060对1.424±0.038),血管密度较小(2.57±0.96对3.92±0.93),血管分支较少(201.87±75.92对271.31±67.37),均P<0.001。随着近视性黄斑病变严重程度的增加,血管角度、Df、血管密度和血管分支显著降低(均P<0.001)。这些特征与AL、BCVA和年龄有显著相关性。mCNV患者的血管密度往往较大(P<0.001),血管分支较多(P=0.045)。

结论

本研究中使用的RU-net和迁移学习技术的准确率为98.24%,因此在超广角图像中血管形态特征的定量分析中具有良好的性能。随着近视性黄斑病变严重程度的增加和眼球的延长,血管角度、Df、血管密度和血管分支减少。近视性CNV患者的血管密度较大,血管分支较多。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/1cb6/9982751/38a502f06e08/fmed-09-956179-g001.jpg

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