Retinal specialist versus artificial intelligence detection of retinal fluid from OCT

Keenan, Tiarnan D.L.; Clemons, Traci E.; Domalpally, Amitha; Elman, Michael J.; Havilio, Moshe; Agrón, Elvira; Benyamini, Gidi; Chew, Emily Y.

doi:10.1016/j.ophtha.2020.06.038

Cited by 93Retinal Imaging and AnalysisRetinal Diseases and TreatmentsRetinal and Macular Surgery

Retinal specialist versus artificial intelligence detection of retinal fluid from OCT

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Keenan, Tiarnan D.L., Clemons, Traci E., Domalpally, Amitha, Elman, Michael J., Havilio, Moshe, Agrón, Elvira, Benyamini, Gidi, Chew, Emily Y.·2021·Ophthalmology

DOI 10.1016/j.ophtha.2020.06.038PMC PMC8371700

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Abstract

Purpose To 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.Design Prospective comparison of retinal fluid grades from human retinal specialists and the Notal OCT Analyzer (NOA) on SD-OCT scans from 2 common devices.Participants A 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).Methods The 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 measures Accuracy (primary), sensitivity, specificity, precision, and F1-score.Results Of 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.Conclusions In 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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A purely clinical approach to monitoring may not capture the full extent or very early signs of GTAU.
“Notwithstanding technological improvements and the integration of artificial intelligence in retinal imaging modalities to increase diagnostic sensitivity (Keenan et al., 2021), a purely clinical approach to monitoring may not capture the full extent or very early signs of GTAU.”
From Gene Therapy-Associated Uveitis (GTAU): Understanding and mitigating the adverse immune response in retinal gene therapy (2025) · cites this paper as Keenan et al., 2021

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“AI-based approaches are currently used to generate real-time estimations of health risks for the diagnosis of various diseases, including skin, liver, and heart diseases, as well as Alzheimer’s [10], in an effort to minimize errors in medical practice involving diagnosis and treatment [11,12].”
Background
VALIDATION OF AN AUTOMATED FLUID ALGORITHM ON REAL-WORLD DATA OF NEOVASCULAR AGE-RELATED MACULAR DEGENERATION OVER FIVE YEARS
Gerendas, B. S., Sadeghipour, A., Michl, M., Goldbach, F., Mylonas, G., Gruber, A., Alten, T., Leingang, O., Sacu, S., Bogunović, H., et al. (2022) · Retina · doi:10.1097/iae.0000000000003557 · Cited by 33 · see citing works
Teleophthalmology and retina: a review of current tools, pathways and services
Than, J., Sim, P. Y., Muttuvelu, D. V., Ferraz, D., Koh, V., Kang, S., & Huemer, J. C. (2023) · International Journal of Retina and Vitreous · doi:10.1186/s40942-023-00502-8 · Cited by 34 · see citing works
Automated deep learning-based AMD detection and staging in real-world OCT datasets (PINNACLE study report 5)
Leingang, O., Riedl, S., Mai, J., Reiter, G. S., Faustmann, G., Fuchs, P., Scholl, H. P. N., Sivaprasad, S., Rueckert, D., Lotery, A. J., et al. (2023) · Scientific Reports · doi:10.1038/s41598-023-46626-7 · Cited by 31 · see citing works
Photobiomodulation Therapy for Age-Related Macular Degeneration and Diabetic Retinopathy: A Review
Muste, J. C., Russell, M. W., & Singh, R. P. (2021) · Clinical ophthalmology · doi:10.2147/opth.s272327 · Cited by 31 · see citing works
Digital Technology for AMD Management in the Post-COVID-19 New Normal
Sim, S. S., Yip, M. Y. T., Wang, Z., Tan, A. C. S., Tan, G. S. W., Cheung, C. M. G., Chakravarthy, U., Wong, T. Y., Teo, K. Y. C., & Ting, D. S. W. (2021) · Asia-Pacific Journal of Ophthalmology · doi:10.1097/apo.0000000000000363 · Cited by 28 · see citing works
“With increasing demands for effective and accurate OCT monitoring, automated techniques have been explored.67,68 These include the Notal OCT Analyzer that reports high-concordance rates when comparing to retinal specialists with an accuracy of 91%, sensitivity of 92%, and specificity of 91%.68 A recent study utilizing…”
MethodResultInfluential
Approved AI-based fluid monitoring to identify morphological and functional treatment outcomes in neovascular age-related macular degeneration in real-world routine
Mares, V., Schmidt-Erfurth, U. M., Leingang, O., Fuchs, P., Nehemy, M. B., Bogunović, H., Barthelmes, D., & Reiter, G. S. (2023) · British Journal of Ophthalmology · doi:10.1136/bjo-2022-323014 · Cited by 27 · see citing works
Evaluation of a Self-Imaging SD-OCT System for Remote Monitoring of Patients with Neovascular Age Related Macular Degeneration
Nahen, K., Benyamini, G., & Loewenstein, A. (2020) · Klinische Monatsblätter für Augenheilkunde · doi:10.1055/a-1271-6834 · Cited by 27 · see citing works
“The Notal OCT Analyzer [12,13] (Notal Vision Inc.”
BackgroundSelf-citation
Advances in artificial intelligence-based technologies for increasing the quality of medical products
Srivastava, N., Verma, S., Singh, A., Shukla, P., Singh, Y., Oza, A. D., Kaur, T., Chowdhury, S., Kapoor, M., & Yadav, A. N. (2024) · DARU Journal of Pharmaceutical Sciences · doi:10.1007/s40199-024-00548-5 · Cited by 27 · see citing works
Gene Therapy-Associated Uveitis (GTAU): Understanding and mitigating the adverse immune response in retinal gene therapy
Purdy, R., John, M., Bray, A., Clare, A. J., Copland, D. A., Chan, Y. K., Henderson, R. H., Nerinckx, F., Leroy, B. P., Yang, P., et al. (2025) · Progress in Retinal and Eye Research · doi:10.1016/j.preteyeres.2025.101354 · Cited by 24 · see citing works
“Notwithstanding technological improvements and the integration of artificial intelligence in retinal imaging modalities to increase diagnostic sensitivity (Keenan et al., 2021), a purely clinical approach to monitoring may not capture the full extent or very early signs of GTAU.”
AI-based support for optical coherence tomography in age-related macular degeneration
Mares, V., Nehemy, M. B., Bogunović, H., Frank‐Publig, S., Reiter, G. S., & Schmidt-Erfurth, U. M. (2024) · International Journal of Retina and Vitreous · doi:10.1186/s40942-024-00549-1 · Cited by 23 · see citing works
Artificial Intelligence, Heuristic Biases, and the Optimization of Health Outcomes: Cautionary Optimism
Feehan, M., Owen, L. A., McKinnon, I. M., & DeAngelis, M. M. (2021) · Journal of Clinical Medicine · doi:10.3390/jcm10225284 · Cited by 23 · see citing works
Evaluation of an Artificial Intelligence-Based Detector of Sub- and Intraretinal Fluid on a Large Set of Optical Coherence Tomography Volumes in Age-Related Macular Degeneration and Diabetic Macular Edema
Habra, O., Gallardo, M., zu Westram, T. M., Zanet, S. I. D., Jaggi, D., Zinkernagel, M. S., Wolf, S., & Sznitman, R. (2022) · Ophthalmologica · doi:10.1159/000527345 · Cited by 23 · see citing works
Current status of home monitoring technology for age-related macular degeneration
Busquets, M. A., & Sabbagh, O. (2021) · Current Opinion in Ophthalmology · doi:10.1097/icu.0000000000000756 · Cited by 20 · see citing works
Artificial Intelligence and Deep Learning in Ophthalmology
Wang, Z., Keane, P. A., Chiang, M. F., Cheung, C. Y.-L., Wong, T. Y., & Ting, D. S. W. (2021) · Springer eBooks · doi:10.1007/978-3-030-58080-3_200-1 · Cited by 20 · see citing works
Personalized treatment supported by automated quantitative fluid analysis in active neovascular age-related macular degeneration (nAMD)—a phase III, prospective, multicentre, randomized study: design and methods
Coulibaly, L. M., Sacu, S., Fuchs, P., Bogunović, H., Faustmann, G., Unterrainer, C., Reiter, G. S., & Schmidt-Erfurth, U. M. (2022) · Eye · doi:10.1038/s41433-022-02154-8 · Cited by 19 · see citing works
“suggested that an AI-algorithm could surpass retinal specialists in accurately detecting fluid pooling, especially IRF and in difficult cases with lower fluid volumes in fewer B-scans [31].”
Background
Quantitative assessment of retinal fluid in neovascular age-related macular degeneration under anti-VEGF therapy
Reiter, G. S., & Schmidt-Erfurth, U. M. (2022) · Therapeutic Advances in Ophthalmology · doi:10.1177/25158414221083363 · Cited by 18 · see citing works
A systematic evaluation of human expert agreement on optical coherence tomography biomarkers using multiple devices
Michl, M., Neschi, M., Kaider, A., Hatz, K. B., Deák, G. G., Gerendas, B. S., & Schmidt-Erfurth, U. M. (2022) · Eye · doi:10.1038/s41433-022-02376-w · Cited by 18 · see citing works
Autoencoder-based phenotyping of ophthalmic images highlights genetic loci influencing retinal morphology and provides informative biomarkers
Sergouniotis, P. I., Diakite, A., Gaurav, K., Allen, N., Aslam, T. M., Atan, D., Barman, S. A., Barrett, J., Bishop, P., Black, G. C. M., et al. (2024) · Bioinformatics · doi:10.1093/bioinformatics/btae732 · Cited by 18 · see citing works
DEVELOPMENT AND VALIDATION OF AN EXPLAINABLE ARTIFICIAL INTELLIGENCE FRAMEWORK FOR MACULAR DISEASE DIAGNOSIS BASED ON OPTICAL COHERENCE TOMOGRAPHY IMAGES
Lv, B., Li, S., Liu, Y., Wang, W., Li, H., Zhang, X., Sha, Y., Yang, X., Yang, Y., Wang, Y., et al. (2021) · Retina · doi:10.1097/iae.0000000000003325 · Cited by 17 · see citing works
Predicting wet age-related macular degeneration (AMD) using DARC (detecting apoptosing retinal cells) AI (artificial intelligence) technology
Corazza, P., Maddison, J., Bonetti, P., Guo, L., Luong, V., Garfinkel, A., Younis, S., & Cordeiro, M. F. (2020) · Expert Review of Molecular Diagnostics · doi:10.1080/14737159.2020.1865806 · Cited by 15 · see citing works
“AI systems of retinal images are advocated in ophthalmology because they enable reliable and quantitative metrics to be obtained rapidly [14].”
Background
Multitask Learning for Activity Detection in Neovascular Age-Related Macular Degeneration
Ayhan, M. S., Faber, H., Kuehlewein, L., INHOFFEN, W., Aliyeva, G., Ziemssen, F., & Berens, P. (2023) · Translational Vision Science & Technology · doi:10.1167/tvst.12.4.12 · Cited by 13 · see citing works
Predictive, preventive, and personalized management of retinal fluid via computer-aided detection app for optical coherence tomography scans
Quek, T. C., Takahashi, K., Kang, H. G., Thakur, S., Deshmukh, M., Tseng, R. M. W. W., Nguyen, H., Tham, Y. C., Rim, T. H., Kim, S. S., et al. (2022) · The EPMA Journal · doi:10.1007/s13167-022-00301-5 · Cited by 13 · see citing works
Retinal fluid quantification using a novel deep learning algorithm in patients treated with faricimab in the TRUCKEE study
Aziz, A. A., Khanani, A. M., Khan, H., Lauer, E., Khanani, I., Mojumder, O., Khanani, Z. A., Khan, H., Gahn, G. M., Graff, J. T., et al. (2024) · Eye · doi:10.1038/s41433-024-03532-0 · Cited by 12 · see citing works
Self-citation
To Treat or Not to Treat? Resolving the Question of Subretinal and Intraretinal Fluid in Age-Related Macular Degeneration: A Narrative Review
Foss, A. J. E., Almeida, D. R. P., Cheung, C. M. G., Ogura, Y., Cock, E. D., & Empeslidis, T. (2025) · Ophthalmology and Therapy · doi:10.1007/s40123-025-01093-3 · Cited by 13 · see citing works
Artificial intelligence-based strategies to identify patient populations and advance analysis in age-related macular degeneration clinical trials
Yaghy, A., Lee, A., Keane, P. A., Keenan, T. D. L., Mendonça, L. S. M., Lee, C. S., Cairns, A. M., Carroll, J., Chen, H., Clark, J., et al. (2022) · Experimental Eye Research · doi:10.1016/j.exer.2022.109092 · Cited by 11 · see citing works
Self-citation
QUANTITATIVE ASSESSMENT OF AUTOMATED OPTICAL COHERENCE TOMOGRAPHY IMAGE ANALYSIS USING A HOME-BASED DEVICE FOR SELF-MONITORING NEOVASCULAR AGE-RELATED MACULAR DEGENERATION
Oakley, J. D., Verdooner, S. R., Russakoff, D. B., Brucker, A. J., Seaman, J. W., Sahni, J., Bianchi, C., Cozzi, M., Rogers, J., & Staurenghi, G. (2022) · Retina · doi:10.1097/iae.0000000000003677 · Cited by 10 · see citing works
Three-Dimensional Map of Descemet Membrane Endothelial Keratoplasty Detachment
Glatz, A., Böhringer, D., Zander, D. B., Grewing, V., Fritz, M., Müller, C., Bixler, S., Reinhard, T., & Wacker, K. (2021) · Ophthalmology Science · doi:10.1016/j.xops.2021.100067 · Cited by 10 · see citing works
Quantitative Multimodal Imaging Characterization of Intraretinal Cysts versus Degenerative Pseudocysts in Neovascular Age-Related Macular Degeneration
Arrigo, A., Aragona, E., Parodi, M. B., & Bandello, F. M. (2024) · Ophthalmology Retina · doi:10.1016/j.oret.2024.05.019 · Cited by 8 · see citing works
The Quantification and Impact of Persistent Retinal Fluid Compartments on Best-Corrected Visual Acuity of Patients With Retinal Vein Occlusion
Muste, J. C., Iyer, A. I., Kalur, A., Talcott, K. E., & Singh, R. P. (2022) · Ophthalmic surgery, lasers & imaging retina · doi:10.3928/23258160-20220215-03 · Cited by 8 · see citing works
Advanced Artificial Intelligence Technologies Transforming Contemporary Pharmaceutical Research
Kumar, P., Chaudhary, B., Arya, P., Chauhan, R., Devi, S., Parejiya, P. B., & Gupta, M. M. (2025) · Bioengineering · doi:10.3390/bioengineering12040363 · Cited by 7 · see citing works
“12 of 35 In order to evaluate the real data samples, preliminary information is used [56]. shared and re ﬁ ned.”
Artificial intelligence (Al) in healthcare diagnosis: evidence-based recent advances and clinical implications
Bhatt, J., Jain, S., & Bhatia, D. D. (2025) · Sensors & Diagnostics · doi:10.1039/d5sd00146c · Cited by 12 · see citing works
INTEGRATING ARTIFICIAL INTELLIGENCE IN DISEASE DIAGNOSIS, TREATMENT, AND FORMULATION DEVELOPMENT: A REVIEW
Kumar, D., Kumar, P., Ahmed, I., & Singh, S. (2023) · Asian Journal of Pharmaceutical and Clinical Research · doi:10.22159/ajpcr.2023.v16i11.48193 · Cited by 7 · see citing works
Comparison of AI ‐based retinal fluid monitoring in neovascular age‐related macular degeneration with manual assessment by different eye care professionals under optimized conditions
Michl, M., Gerendas, B. S., Gruber, A., Goldbach, F., Mylonas, G., Leingang, O., Buehl, W., Sacu, S., Bogunović, H., Sadeghipour, A., et al. (2025) · Acta Ophthalmologica · doi:10.1111/aos.17458 · Cited by 6 · see citing works
“This problem is aggravated by substantial disagreements even between experienced ophthalmologists when assessing retinal fluid, especially in patients with little macular fluid or IRF only (Keenan et al., 2021; Toth et al., 2015).”
A clinical perspective on the expanding role of artificial intelligence in age-related macular degeneration
Kumar, H., Goh, K. L., Guymer, R. H., & Wu, Z. (2022) · Clinical and Experimental Optometry · doi:10.1080/08164622.2021.2022961 · Cited by 6 · see citing works
Harnessing artificial intelligence (AI) techniques for the diagnosis of microbial disease in humans
Ogidi, O. I., & Tobia, P.-E. S. (2024) · Methods in microbiology · doi:10.1016/bs.mim.2024.05.004 · Cited by 5 · see citing works
Analysis of OCT Scanning Parameters in AMD and RVO
von der Burchard, C. C., Roider, J. B., & Kepp, T. (2024) · Diagnostics · doi:10.3390/diagnostics14050516 · Cited by 5 · see citing works
“Regarding individual biomarkers, the authors did not present distribution details in the reference OCT but focused solely on detection rates using the investigational device.”
Method
Quantifying Changes on OCT in Eyes Receiving Treatment for Neovascular Age-Related Macular Degeneration
Moraes, G., Struyven, R., Wagner, S. K., Liu, T., Chong, D., Abbas, A., Chopra, R., Patel, P. J., Balaskas, K., Keenan, T. D. L., et al. (2024) · Ophthalmology Science · doi:10.1016/j.xops.2024.100570 · Cited by 6 · see citing works
Self-citation
Use of Home Optical Coherence Tomography for Retinal Diseases
Bordbar, D. D., Bhatnagar, A., & Weng, C. Y. (2024) · International Ophthalmology Clinics · doi:10.1097/iio.0000000000000546 · Cited by 5 · see citing works
Deep learning based retinal disease classification using an autoencoder and generative adversarial network
Jeyasri, G., & Karthiyayini, R. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107852 · Cited by 5 · see citing works
Performance of a Machine-Learning Computational Image Analysis Algorithm in Retinal Fluid Quantification for Patients With Diabetic Macular Edema and Retinal Vein Occlusions
Song, W., Kaakour, A.-H., Kalur, A., Muste, J. C., Iyer, A. I., Valentim, C. C. S., & Singh, R. P. (2022) · Ophthalmic surgery, lasers & imaging retina · doi:10.3928/23258160-20220215-02 · Cited by 5 · see citing works
Retinal Pigment Epithelium Curvature Can Predict Late Age-Related Macular Degeneration
Cheung, R., Trinh, M., & Nivison‐Smith, L. (2024) · Investigative Ophthalmology & Visual Science · doi:10.1167/iovs.65.12.7 · Cited by 4 · see citing works
Comparing Ophthalmologist and Artificial Intelligence Chatbot Responses to Patient Questions
Bondok, M., Selvakumar, R., Law, C., Ing, E. B., Bakshi, N. K., & Felfeli, T. (2025) · Clinical ophthalmology · doi:10.2147/opth.s549820 · Cited by 5 · see citing works
Machine Learning Algorithms for the Analysis of Age-Related Macular Degeneration Based on Optical Coherence Tomography: a Systematic Review
Lopukhova, E. A., Ibragimova, R. R., Маратовна, И. Г., Lakman, I. A., Рафаэльевич, М. Т., Grakhova, E. P., Bilyalov, A. R., & Kutluyarov, R. V. (2023) · Journal of Biomedical Photonics & Engineering · doi:10.18287/jbpe23.09.020202 · Cited by 4 · see citing works
Human expert grading versus automated quantification of fluid volumes in nAMD, DME and BRVO
Goldbach, F., Gerendas, B. S., Leingang, O., Alten, T., Bampoulidis, A., Brugger, J., Bogunović, H., Sadeghipour, A., & Schmidt-Erfurth, U. M. (2025) · Scientific Reports · doi:10.1038/s41598-025-13019-x · Cited by 4 · see citing works
Impact of AI-quantified fluid dynamics on visual outcomes over 5 years in patients with treatment-naïve nAMD from the FRB! registry
Fuchs, P., Schmidt-Erfurth, U. M., Coulibaly, L. M., Bogunović, H., Leingang, O., Gruber, A., Frommlet, F., Mares, V., Barthelmes, D., & Reiter, G. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-17417-z · Cited by 3 · see citing works
Characterizing Early Residual Fluid in Neovascular Age-Related Macular Degeneration using Machine Learning in Routine Clinical Practice
Wu, A. K., Perkins, S. W., & Singh, R. P. (2022) · Ophthalmology Retina · doi:10.1016/j.oret.2022.06.006 · Cited by 3 · see citing works
An Analysis of the Usage of Retinal Imaging Technology in the Detection of Age-Related Macular Degeneration
Esfahani, P. R., Reddy, A. J., Thomas, J., Sommer, D. A., Nguyen, A., Farasat, V. P., Nawathey, N., Bachir, A., Brahmbhatt, T., & Patel, R. (2023) · Cureus · doi:10.7759/cureus.40527 · Cited by 3 · see citing works
“Among all of the studies analyzed in this review, based on the information presented in Table 1, diabetes mellitus (DM) with or without progressed diabetic retinopathy (DR) was by far the most prevalent previous medical condition noted in patients diagnosed with AMD [6-29].”
BackgroundMethodInfluential
Predictors of limited early response to anti-vascular endothelial growth factor therapy in neovascular age-related macular degeneration with machine learning feature importance
Perkins, S. W., Wu, A. K., & Singh, R. P. (2022) · Saudi Journal of Ophthalmology · doi:10.4103/sjopt.sjopt_73_22 · Cited by 2 · see citing works
“, Tel Aviv, Israel), a validated ML algorithm that automatically quantifies IRF and SRF in nAMD.[13,14] LER was defined as the presence of IRF and/or SRF at month 3 whereas early response (ER) was defined as the absence of fluid.”
Background
Evaluating the clinical relevance and reliability of outer retinal band length on optical coherence tomography in retinal disease: a cross-sectional study
Cheung, R., Ly, A., Wang, H., Kalloniatis, M., & Nivison‐Smith, L. (2023) · BMJ Open · doi:10.1136/bmjopen-2023-077874 · Cited by 2 · see citing works
Circapproved: Digital Pattern Recognition via Artificial Neural Network for the Identification of Normal Penis Parameters for Circumcision Eligibility Using Mobile App
Wahyudi, I., Utomo, C. P., Djauzi, S., Fathurahman, M., Situmorang, G. R., Rodjani, A., Raharja, P. A. R., Yonathan, K., Santoso, B., Khresna, D., et al. (2025) · Journal of Pediatric Surgery · doi:10.1016/j.jpedsurg.2025.162358 · Cited by 2 · see citing works
Dynamics and patterns of recurrence in neovascular AMD during real-world management using automated fluid monitoring
Prenner, V., Schmidt-Erfurth, U. M., Fuchs, P., Leingang, O., Coulibaly, L. M., Bogunović, H., Barthelmes, D., & Reiter, G. S. (2024) · Heliyon · doi:10.1016/j.heliyon.2024.e31567 · Cited by 1 · see citing works
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
Busquets, M. A., Garfinkel, R. A., Sambhara, D., Mohan, N., Nahen, K., Benyamini, G., & Loewenstein, A. (2025) · Medicina · doi:10.3390/medicina61122193 · Cited by 1 · see citing works
Self-citation
Meta-Analysis on Comparison of Diagnostic Accuracy Between Artificial Intelligence and Healthcare Professionals
Kumar, P., Alnaimi, N. A. A., Soman, S., Suansing, L., Arriola, D. R., & Jamea, L. A. (2026) · Sci · doi:10.3390/sci8040073 · Cited by 1 · see citing works
“%) were randomized control trials (Baek 2025 [40]; Boginskis 2023 [13]; Camkıran 2025 [35]; Choi 2020 [33]; Faqar-Uz-Zaman 2022 [37]; Han 2022 [29]; Harada 2021 [24]; Homayounieh 2021 [21]; Keenan 2020 [44]; Liu 2022 [25]; Liu 2023 [27]; Luo 2021 [43]; Wang 2021 [22]), seven (21”
Influential
The future of health diagnosis and treatment: an exploration of deep learning frameworks and innovative applications
Zafar, I., Ahmad, S. U., Rather, M. A., Khan, A. A., Ain, Q. U., Safder, A., & Sehgal, S. A. (2023) · Elsevier eBooks · doi:10.1016/b978-0-443-19413-9.00002-3 · Cited by 1 · see citing works
A State-of-the-art Review on Artificial Intelligence and Machine Learning Applications
Sharma, R., Malviya, R., Uniyal, P., & Prajapati, B. G. (2024) · Current Drug Therapy · doi:10.2174/0115748855297767240408053500 · Cited by 1 · see citing works
Impact of Persistent Retinal Fluid in Patients with Neovascular Age-Related Macular Degeneration in Routine Clinical Practice
Iyer, A. I., Muste, J. C., Kalur, A., Talcott, K. E., & Singh, R. P. (2022) · Ophthalmic surgery, lasers & imaging retina · doi:10.3928/23258160-20220602-02
Multiscale attention network for retinal vein occlusion classification with multicolor image
Wang, X., Ding, Y., & Zheng, Y. (2023) · International Journal of Imaging Systems and Technology · doi:10.1002/ima.22917
Integrating Artificial Intelligence for Enhanced Tuberculosis Diagnosis and Management: A Comprehensive Analysis
Vyas, A., Singh, A. P., Dave, A., & Dave, S. (2024) · Advanced technologies and societal change · doi:10.1007/978-981-97-3312-5_5
Artificial intelligence 7: Language models, including ChatGPT, for use in optometric practice, education and business operations
Devraj, K., & Huntjens, B. (2024) · Optician · doi:10.12968/opti.2024.270.6982.26
Cochrane corner: artificial intelligence for diagnosing exudative age-related macular degeneration
Vedula, S. S., & Li, T. (2025) · Eye · doi:10.1038/s41433-025-03948-2
Additional file 1 of Evaluation of a self-imaging SD-OCT system designed for remote home monitoring
Kim, J. E., Tomkins‐Netzer, O., Elman, M. J., Lally, D. R., Goldstein, M., Goldenberg, D., Shulman, S., Benyamini, G., & Loewenstein, A. (2022) · Figshare · doi:10.6084/m9.figshare.20056201
Self-citation
Additional file 2 of Evaluation of a self-imaging SD-OCT system designed for remote home monitoring
Kim, J. E., Tomkins‐Netzer, O., Elman, M. J., Lally, D. R., Goldstein, M., Goldenberg, D., Shulman, S., Benyamini, G., & Loewenstein, A. (2022) · Figshare · doi:10.6084/m9.figshare.20056204
Self-citation
ARTIFICIAL INTELLIGENCE IN AGE-RELATED MACULAR DEGENERATION: POTENTIAL CLINICAL IMPLICATION
Kępczyńska, Z., Kubik, W., Czarnecki, B., Nowak, J., Kujawa, B., Zwoliński, B., Sukiennicki, K., Bertman, W., Kołdej, N., Szewczyk, K., et al. (2025) · International Journal of Innovative Technologies in Social Science · doi:10.31435/ijitss.4(48).2025.4351
Home and Community-Based Screening Tools for Age-Related Macular Degeneration
Saini, A., Lee, T., Iftikhar, M., Hadziahmetovic, M., & Vajzovic, L. M. (2026) · Current Ophthalmology Reports · doi:10.1007/s40135-026-00348-x
Automatic selection of optical coherence tomography images for prognostic prediction models in age-related macular degeneration
Deng, Q., Kishimoto, K., Sugiyama, O., Miyake, M., & Tamura, H. (2026) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2026.109384
Early Structure–Function Correlates in Neovascular Age-Related Macular Degeneration Using Artificial Intelligence–Derived OCT Biomarkers
Mares, V., Sacu, S., Frank‐Publig, S., Eidenberger, A., Fuchs, P., Coulibaly, L. M., Baratsits, M., Faustmann, G., Gumpinger, M., Sükei, E., et al. (2026) · Ophthalmology Science · doi:10.1016/j.xops.2026.101281
RetinaGuardx: A Hybrid Model with Grad CAM for Retinal Disease Detection
Jameer, S., Sravanthi, K., Lakshmi, K. S., Babu, C. L., & Kumar, R. P. S. (2026) · Algorithms for intelligent systems · doi:10.1007/978-3-032-12243-8_11
Classification of Inherited Retinal Diseases Using Artificial Intelligence Models for Fundus Autofluorescence and Ultrawide Retinal Images
Trinh, H., Ibrahim, M. M., Charng, J., Tajbakhsh, Z., Chen, F. K., Mian, A. S., & Alam, K. (2026) · Journal of Ophthalmology · doi:10.1155/joph/8810684
Deep Learning-Based Automated Detection, Segmentation, and Quantification of Retinal Fluid on Optical Coherence Tomography: A Validation Study
Hasan, N., Gadari, A., Vupparaboina, S. C., Lall, S. R., Emfietzoglou, M., Peña, D. F., Gregori, G., Vichare, A. A., Doshi, U., Sadeghi, E., et al. (2026) · Diagnostics · doi:10.3390/diagnostics16213497

Evaluation of an Artificial Intelligence-based Detector of Sub- and Intra-Retinal Fluid on a large set of OCT volumes in AMD and DME
Habra, O., Gallardo, M., Meyer zu Westram, T., Zanet, S. I. D., Jaggi, D., Zinkernagel, M. S., Wolf, S., & Sznitman, R. (2022) · doi:10.48350/173747
Evaluation of an Artificial Intelligence-Based Detector of Sub- and Intraretinal Fluid on a Large Set of Optical Coherence Tomography Volumes in Age-Related Macular Degeneration and Diabetic Macular Edema
Habra, O., Gallardo, M., zu Westram, T. M., Zanet, S. I. D., Jaggi, D., Zinkernagel, M. S., Wolf, S., & Sznitman, R. (2022) · Ophthalmologica · doi:10.1159/000527345 · Cited by 23 · see citing works
Automated Identification of Lesion Activity in Neovascular Age-Related Macular Degeneration
Chakravarthy, U., Goldenberg, D., Young, G., Havilio, M., Rafaeli, O., Benyamini, G., & Loewenstein, A. (2016) · Ophthalmology · doi:10.1016/j.ophtha.2016.04.005 · Cited by 113 · see citing works
Self-citation
Comparison of AI ‐based retinal fluid monitoring in neovascular age‐related macular degeneration with manual assessment by different eye care professionals under optimized conditions
Michl, M., Gerendas, B. S., Gruber, A., Goldbach, F., Mylonas, G., Leingang, O., Buehl, W., Sacu, S., Bogunović, H., Sadeghipour, A., et al. (2025) · Acta Ophthalmologica · doi:10.1111/aos.17458 · Cited by 6 · see citing works
Artificial intelligence algorithm may improve performance of retinal specialists in detection of retinal fluid on OCT in age-related macular degeneration: AREDS2 10-Year follow-on Study
Elman, M. J., Keenan, T. D. L., Clemons, T. E., Domalpally, A., Havilio, M., Benyamini, G., & Chew, E. Y. (2020) · Investigative Ophthalmology & Visual Science
Self-citation

Authors: Keenan, T. D. L., Clemons, T. E., Domalpally, A., Elman, M. J., Havilio, M., Agrón, E., Benyamini, G., & Chew, E. Y.
Affiliations: Emmes (United States) (US)
University of Wisconsin–Madison (US)
Elman Retina Group (US)
Notal Vision (Israel) (IL)
National Institutes of Health (US)
ORCID: Clemons, Traci E.
Domalpally, Amitha
Elman, Michael J.
Agrón, Elvira
Benyamini, Gidi
Chew, Emily Y.
Venue: Ophthalmology
Publisher: Elsevier BV
Published: 2021-01
Volume: 128
Issue: 1
Pages: 100-109
ISSN: 0161-6420 (ISSN-L)
DOI: 10.1016/j.ophtha.2020.06.038
PMC: PMC8371700
OpenAlex: W3046617692
Cited by: 93
References: 40
Citations by year: 2026: 7 · 2025: 15 · 2024: 17 · 2023: 13 · 2022: 24 · 2021: 14 · 2020: 2 · 2018: 1
FWCI: 6.45
Type: journal article
Language: en
Open access: green
www.ncbi.nlm.nih.gov
Licence: www.elsevier.com/tdm/userlicense/1.0/ (tdm)
www.elsevier.com/legal/tdmrep-license (tdm)
Locations: Ophthalmology (publishedVersion)
PubMed (publishedVersion)
PubMed Central (acceptedVersion)
Funders: National Eye Institute, National Institutes of Health
Department of Health and Human Services
Topics: Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal Imaging and Analysis
Health Sciences › Medicine › Ophthalmology › Retinal Diseases and Treatments
Health Sciences › Medicine › Radiology, Nuclear Medicine and Imaging › Retinal and Macular Surgery
Keywords: age-related macular degeneration, spectral-domain optical coherence tomography, artificial intelligence, intraretinal fluid, subretinal fluid, automated grading, diagnostic accuracy
MeSH terms: Ophthalmologists, Aged, 80 and over, Artificial Intelligence, Female, Follow-Up Studies, Humans, Macular Degeneration, Male, Prospective Studies, Time Factors, Tomography, Optical Coherence, Subretinal Fluid

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Retinal specialist versus artificial intelligence detection of retinal fluid from OCT

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