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

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.