ArticleScientific reports2025
Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers, 1 of them a synthesis that pooled it.
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Who cites it
3 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Baseline OCT biomarkers associated with visual prognosis in cystoid macular edema secondary to noninfectious uveitis.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
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Authors and funding
9 authors.
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No grant is acknowledged in the PubMed record.
Abstract
This study compared an automated deep learning algorithm with certified human graders from the Vienna Reading Center (VRC) in identifying intra- (IRF) and subretinal fluid (SRF) in OCT scans of patients treated for neovascular age-related macular degeneration (nAMD), diabetic macular edema (DME) and branch retinal vein occlusion (BRVO). Multicenter clinical trial data from the VRC imaging database was used for this post hoc analysis. OCT scans were analyzed using a validated algorithm (RetInSight, Vienna, Austria) to compute IRF and SRF volumes. These fluid volumes were compared to fluid presence graded by trained and experienced graders of the VRC. 6898 OCT scans were analyzed for fluid volumes and presence of IRF and SRF. For nAMD/DME /BRVO in the central millimeter: the overall concordance for the detection of IRF and SRF between the algorithm and manual grading reached an AUC of 0.94/0.92/0.98 and 0.89/0.95/0.92, respectively. This deep learning approach showed a high concordance with human expert grading for detection of IRF and SRF and provides precise volumetric information across different retinal fluid-associated diseases. Thus, automated fluid quantification is a feasible tool for standardized treatment decision support and disease monitoring in clinical practice at the highest human expert level.
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