Trial reportOphthalmology2021
Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.
Trial report in Ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 47 papers, 1 of them a synthesis that pooled it.
What it found
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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
47 citing papers in PubMed, 1 synthesis or guideline pooled it, 90 citations in OpenAlex.
- Optical coherence tomography imaging biomarkers associated with neovascular age-related macular degeneration: a systematic review.Eye (London, England) · 2023Pooled it
- Therapeutic response in the HAWK and HARRIER trials using deep learning in retinal fluid volume and compartment analysis.Eye (London, England) · 2023Trial
- Early Structure-Function Correlates in Neovascular Age-Related Macular Degeneration Using Artificial Intelligence-Derived OCT Biomarkers.Ophthalmology science · 2026Article
- Clinical application of automated optical coherence tomography angiography for retinal fluid segmentation: a study of real-world data.Quantitative imaging in medicine and surgery · 2026Article
- Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images.Journal of ophthalmology · 2026Article
- Home Monitoring for the Management of Age-Related Macular Degeneration: A Review of the Development and Implementation of Digital Health Solutions over a 25-Year Scientific Journey.Medicina (Kaunas, Lithuania) · 2025Review
- Cochrane corner: artificial intelligence for diagnosing exudative age-related macular degeneration.Eye (London, England) · 2025Article
- Impact of AI-quantified fluid dynamics on visual outcomes over 5 years in patients with treatment-naïve nAMD from the FRB! registry.Scientific reports · 2025Article
- Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO.Scientific reports · 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
- Age-Related Macular Degeneration: Cellular and Molecular Signaling Mechanisms.International journal of molecular sciences · 2025Review
- Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study.Eye (London, England) · 2025Article
- Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research.Bioengineering (Basel, Switzerland) · 2025Review
- To Treat or Not to Treat? Resolving the Question of Subretinal and Intraretinal Fluid in Age-Related Macular Degeneration: A Narrative Review.Ophthalmology and therapy · 2025Review
- Comparing Ophthalmologist and Artificial Intelligence Chatbot Responses to Patient Questions.Clinical ophthalmology (Auckland, N.Z.) · 2025Article
- Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers.Bioinformatics (Oxford, England) · 2024Article
- Advances in artificial intelligence-based technologies for increasing the quality of medical products.Daru : journal of Faculty of Pharmacy, Tehran University of Medical Sciences · 2024Review
- Retinal Pigment Epithelium Curvature Can Predict Late Age-Related Macular Degeneration.Investigative ophthalmology & visual science · 2024Article
- Dynamics and patterns of recurrence in neovascular AMD during real-world management using automated fluid monitoring.Heliyon · 2024Article
- AI-based support for optical coherence tomography in age-related macular degeneration.International journal of retina and vitreous · 2024Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
8 authors at 5 institutions in 2 countries.
Funding
Abstract
purposeTo evaluate the performance of retinal specialists in detecting retinal fluid presence in spectral domain OCT (SD-OCT) scans from eyes with age-related macular degeneration (AMD) and compare performance with an artificial intelligence algorithm.
designProspective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices.
participantsA total of 1127 eyes of 651 Age-Related Eye Disease Study 2 10-year Follow-On Study (AREDS2-10Y) participants with SD-OCT scans graded by reading center graders (as the ground truth).
methodsThe AREDS2-10Y investigators graded each SD-OCT scan for the presence/absence of intraretinal and subretinal fluid. Separately, the same scans were graded by the NOA.
main outcome measuresAccuracy (primary), sensitivity, specificity, precision, and F1-score.
resultsOf the 1127 eyes, retinal fluid was present in 32.8%. For detecting retinal fluid, the investigators had an accuracy of 0.805 (95% confidence interval [CI], 0.780-0.828), a sensitivity of 0.468 (95% CI, 0.416-0.520), a specificity of 0.970 (95% CI, 0.955-0.981). The NOA metrics were 0.851 (95% CI, 0.829-0.871), 0.822 (95% CI, 0.779-0.859), 0.865 (95% CI, 0.839-0.889), respectively. For detecting intraretinal fluid, the investigator metrics were 0.815 (95% CI, 0.792-0.837), 0.403 (95% CI, 0.349-0.459), and 0.978 (95% CI, 0.966-0.987); the NOA metrics were 0.877 (95% CI, 0.857-0.896), 0.763 (95% CI, 0.713-0.808), and 0.922 (95% CI, 0.902-0.940), respectively. For detecting subretinal fluid, the investigator metrics were 0.946 (95% CI, 0.931-0.958), 0.583 (95% CI, 0.471-0.690), and 0.973 (95% CI, 0.962-0.982); the NOA metrics were 0.863 (95% CI, 0.842-0.882), 0.940 (95% CI, 0.867-0.980), and 0.857 (95% CI, 0.835-0.877), respectively.
conclusionsIn this large and challenging sample of SD-OCT scans obtained with 2 common devices, retinal specialists had imperfect accuracy and low sensitivity in detecting retinal fluid. This was particularly true for intraretinal fluid and difficult cases (with lower fluid volumes appearing on fewer B-scans). Artificial intelligence-based detection achieved a higher level of accuracy. This software tool could assist physicians in detecting retinal fluid, which is important for diagnostic, re-treatment, and prognostic tasks.
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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.