Comparison of AI-based retinal fluid monitoring in neovascular age-related macular degeneration with manual assessment by different eye care professionals under optimized conditions

在优化条件下,比较基于人工智能的视网膜液监测与不同眼科专业人员手动评估在新生血管性年龄相关性黄斑变性中的应用。

阅读:2

Abstract

PURPOSE: To investigate whether automated intra- and subretinal fluid (IRF/SRF) volume measurements are equivalent to manual evaluations by eye care professionals from different backgrounds on real-world optical coherence tomography (OCT) images in neovascular age-related macular degeneration (nAMD). METHODS: Routine OCT images (Spectralis, Heidelberg Engineering) were obtained during standard-of-care anti-VEGF treatment for nAMD at a tertiary referral centre. IRF/SRF presence and change (increase/decrease/stability) were assessed without time constraints by five retinologists, three ophthalmology residents, three general ophthalmologists, three orthoptists and three certified readers. Fluid volumes were segmented and quantified using a regulatory-approved AI-based tool (Vienna Fluid Monitor, RetInSight, Vienna, Austria). Sensitivity/specificity (Sen/Spe) for grading fluid presence and kappa agreement were calculated for each group. Their performances in distinguishing between IRF/SRF increase and decrease were assessed using AUCs. RESULTS: About 124 follow-up visit pairs of 59 eyes with active nAMD were included. Across all five groups, fluid volumes >5 nL were identified with values of 0.81-0.95 (Sen)/0.70-0.91 (Spe) for IRF and 0.89-0.98 (Sen)/0.74-0.90 (Spe) for SRF. Interpretations of IRF changes between -17 nL and +3 nL and SRF changes between -9.30 nL and +6.50 nL were associated with Sen > 0.80 and Spe > 0.87 among all groups. Agreements between the algorithm and groups in grading IRF/SRF presence ranged from κ = 0.69-0.82/0.73-0.79. The AUC for correctly classifying fluid change was >0.89 across all groups. CONCLUSION: Eye care professionals with different levels of clinical expertise assessed disease activity on standard OCT images with comparable accuracy. Despite optimizing the methodology and time resources, manual performance did not reach the high level of automated fluid monitoring.

特别声明

1、本页面内容包含部分的内容是基于公开信息的合理引用;引用内容仅为补充信息,不代表本站立场。

2、若认为本页面引用内容涉及侵权,请及时与本站联系,我们将第一时间处理。

3、其他媒体/个人如需使用本页面原创内容,需注明“来源:[生知库]”并获得授权;使用引用内容的,需自行联系原作者获得许可。

4、投稿及合作请联系:info@biocloudy.com。