ArticleJournal of clinical medicine2023
Validation of an Automated Artificial Intelligence Algorithm for the Quantification of Major OCT Parameters in Diabetic Macular Edema.
Article in Journal of clinical medicine, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 28 papers.
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Who cites it
28 citing papers in PubMed, 43 citations in OpenAlex.
- Deep learning-based prediction of one-year functional and structural outcomes in treatment-naïve DMO after anti-VEGF therapy.Eye (London, England) · 2026Article
- The Behavior of the External Limiting Membrane and Ellipsoid Zone in Diabetic Macular Edema: A Population AI-Assisted Optical Coherence Tomography Study.Investigative ophthalmology & visual science · 2026Article
- Artificial intelligence-based fluid quantification predicts clinical outcomes in diabetic macular oedema eyes treated with intravitreal dexamethasone implants: DIADEMA project.The British journal of ophthalmology · 2026Article
- Functional and Structural Outcomes of Photodynamic Therapy (PDT) With or Without Eplerenone for Central Serous Chorioretinopathy.Journal of clinical medicine · 2026Article
- Prognostic Imaging Biomarkers in Diabetic Macular Edema Treated with Anti-VEGF: A Multicenter AI Perspective.Ophthalmology and therapy · 2026Article
- Validation of an Automated AI Algorithm for the Quantification of Major OCT Parameters in Retinal Vein Occlusion-Related Macular Edema.Journal of clinical medicine · 2026Article
- Scan Density Matters: Reproducibility of AI-Derived OCT Biomarkers in Diabetic Macular Edema.Translational vision science & technology · 2026Article
- U-Net Optimization for Hyperreflective Foci Segmentation in Retinal OCT.Diagnostics (Basel, Switzerland) · 2026Article
- Automated Feature Segmentation of Ultra-Widefield OCT Images.Ophthalmology science · 2026Article
- Anatomical and Systemic Predictors of Early Response to Subthreshold Micropulse Laser in Diabetic Macular Edema: A Retrospective Cohort Study.Journal of clinical medicine · 2026Article
- What Lies beneath Diabetic Macular Edema: Latent Phenotypic Clustering and Differential Treatment Responses to Intravitreal Therapies.Ophthalmology science · 2026Article
- The clinical relevance of hyper-reflective foci in the inner retina at the diagnosis of multiple sclerosis.Neurological research and practice · 2025Article
- AI-Assisted OCT Clinical Phenotypes of Diabetic Macular Edema: A Large Cohort Clustering Study.Journal of clinical medicine · 2025Article
- Neuroinflammation in Radiation Maculopathy: A Pathophysiologic and Imaging Perspective.Cancers · 2025Review
- Diabetic Macular Edema in Maintenance Intravitreal Scheduling.Pharmaceutics · 2025Article
- Hyperreflective Retinal Foci (HRF): Definition and Role of an Invaluable OCT Sign.Journal of clinical medicine · 2025Article
- Letter to the Editor Regarding "Intraretinal Hyper-Reflective Foci Are Almost Universally Present and Co-Localize With Intraretinal Fluid in Diabetic Macular Edema".Investigative ophthalmology & visual science · 2025Article
- AI-driven strategies for advancing corneal cell therapy: a promising frontier.Frontiers in medicine · 2025Review
- Analysis of OCT-Based Biomarkers and Recurrence in Eyes with Diabetic Macular Edema Following Anti-VEGF Therapy.International journal of general medicine · 2025Article
- Integrating TBSRTC subcategorization andFrontiers in oncology · 2025Article
Corrections and comments
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Authors and funding
11 authors at 4 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Artificial intelligence (AI) and deep learning (DL)-based systems have gained wide interest in macular disorders, including diabetic macular edema (DME). This paper aims to validate an AI algorithm for identifying and quantifying different major optical coherence tomography (OCT) biomarkers in DME eyes by comparing the algorithm to human expert manual examination. Intraretinal (IRF) and subretinal fluid (SRF) detection and volumes, external limiting-membrane (ELM) and ellipsoid zone (EZ) integrity, and hyperreflective retina foci (HRF) quantification were analyzed. Three-hundred three DME eyes were included. The mean central subfield thickness was 386.5 ± 130.2 µm. IRF was present in all eyes and confirmed by AI software. The agreement (kappa value) (95% confidence interval) for SRF presence and ELM and EZ interruption were 0.831 (0.738-0.924), 0.934 (0.886-0.982), and 0.936 (0.894-0.977), respectively. The accuracy of the automatic quantification of IRF, SRF, ELM, and EZ ranged between 94.7% and 95.7%, while accuracy of quality parameters ranged between 99.0% (OCT layer segmentation) and 100.0% (fovea centering). The Intraclass Correlation Coefficient between clinical and automated HRF count was excellent (0.97). This AI algorithm provides a reliable and reproducible assessment of the most relevant OCT biomarkers in DME. It may allow clinicians to routinely identify and quantify these parameters, offering an objective way of diagnosing and following DME eyes.
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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 Socratic graph.