ArticleActa ophthalmologica2025
Comparison of AI-based retinal fluid monitoring in neovascular age-related macular degeneration with manual assessment by different eye care professionals under optimized conditions.
Article in Acta ophthalmologica, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
7 citing papers in PubMed.
- Artificial Intelligence-based characterization of therapeutic response in fluid types and volumes influencing retinal function in neovascular age-related macular degeneration.Scientific reports · 2026Trial
- Fluid Volumes Longitudinal Modeling to Predict Atrophy and Fibrosis in Neovascular Age-Related Macular Degeneration.Ophthalmology science · 2026Article
- Biomarker-Specific Test-Retest Repeatabilities of Microperimetry in Neovascular Age-Related Macular Degeneration.Ophthalmology science · 2026Article
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Intravitreal faricimab in patients with aflibercept-refractory neovascular age-related macular degeneration: short and long-term outcomes and assessment of volume dynamics using an artificial intelligence-based tool.International journal of retina and vitreous · 2025Article
- Comparison of AI-based retinal fluid monitoring in neovascular age-related macular degeneration with manual assessment by different eye care professionals under optimized conditions.Acta ophthalmologica · 2025Article
- Comparison of Choroidal Vascular Index Measurements in Pachychoroid Pigment Epitheliopathy using Manual ImageJ-Based Niblack and Protocol-Standardized Otsu Thresholding.Journal of current ophthalmologyArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
11 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
purposeTo 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).
methodsRoutine 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.
resultsAbout 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.
conclusionEye 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.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.