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基于深度学习的量化流体研究中液体量分析及其对视敏度的影响。

ANALYSIS OF FLUID VOLUME AND ITS IMPACT ON VISUAL ACUITY IN THE FLUID STUDY AS QUANTIFIED WITH DEEP LEARNING.

机构信息

Department of Ophthalmology and Optometry, Christian Doppler Laboratory for Ophthalmic Image Analysis, Medical University of Vienna, Vienna, Austria.

Department of Surgery (Ophthalmology), Centre for Eye Research Australia, Royal Victorian Eye and Ear Hospital, and University of Melbourne, Melbourne, Australia; and.

出版信息

Retina. 2021 Jun 1;41(6):1318-1328. doi: 10.1097/IAE.0000000000003023.

Abstract

PURPOSE

To investigate quantitative differences in fluid volumes between subretinal fluid (SRF)-tolerant and SRF-intolerant treat-and-extend regimens for neovascular age-related macular degeneration and analyze the association with best-corrected visual acuity.

METHODS

Macular fluid (SRF and intraretinal fluid) was quantified on optical coherence tomography volumetric scans using a trained and validated deep learning algorithm. Fluid volumes and complete resolution was automatically assessed throughout the study. The impact of fluid location and volumes on best-corrected visual acuity was computed using mixed-effects regression models.

RESULTS

Baseline fluid quantifications for 348 eyes from 348 patients were balanced (all P > 0.05). No quantitative differences in SRF/intraretinal fluid between the treatment arms was found at any study-specific time point (all P > 0.05). Compared with qualitative assessment, the proportion of eyes without SRF/intraretinal fluid did not differ between the groups at any time point (all P > 0.05). Intraretinal fluid in the central 1 mm and SRF in the 1-mm to 6-mm macular area were negatively associated with best-corrected visual acuity (-2.8 letters/100 nL intraretinal fluid, P = 0.007 and -0.20 letters/100 nL SRF, P = 0.005, respectively).

CONCLUSION

Automated fluid quantification using artificial intelligence allows objective and precise assessment of macular fluid volume and location. Precise determination of fluid parameters will help improve therapeutic efficacy of treatment in neovascular age-related macular degeneration.

摘要

目的

研究耐(subretinal fluid, SRF)和不耐(subretinal fluid, SRF)治疗及扩展方案治疗新生血管性年龄相关性黄斑变性(neovascular age-related macular degeneration)的眼内液体积定量差异,并分析其与最佳矫正视力的关系。

方法

使用经过训练和验证的深度学习算法对光学相干断层扫描容积扫描中的黄斑液(SRF 和视网膜内液)进行定量。在整个研究过程中自动评估液体积和完全消退情况。使用混合效应回归模型计算液体积定位和液体积对最佳矫正视力的影响。

结果

348 例患者 348 只眼的基线液体积定量化结果平衡(均 P>0.05)。在任何特定研究时间点,治疗组之间均未发现 SRF/视网膜内液定量差异(均 P>0.05)。与定性评估相比,任何时间点两组之间均无眼内无 SRF/视网膜内液的比例差异(均 P>0.05)。中央 1mm 内视网膜内液和 1mm 至 6mm 黄斑区 SRF 与最佳矫正视力呈负相关(-2.8 个字母/100nL 视网膜内液,P=0.007 和 -0.20 个字母/100nL SRF,P=0.005)。

结论

使用人工智能进行自动液体积定量可对黄斑液体积和位置进行客观、精确的评估。精确确定液体积参数将有助于提高新生血管性年龄相关性黄斑变性的治疗疗效。

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