Observational studyJAMA ophthalmology2021
Comparison of Central Macular Fluid Volume With Central Subfield Thickness in Patients With Diabetic Macular Edema Using Optical Coherence Tomography Angiography.
Observational study in JAMA ophthalmology, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
24 citing papers in PubMed, 1 synthesis or guideline pooled it, 31 citations in OpenAlex.
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- Association between macular edema area on optical coherence tomography and Amsler grid sensitivity in diabetic macular edema: a cross-sectional study.BMC ophthalmology · 2026Article
- Optical Coherence Tomography Biomarkers as Potential Predictors for Visual Function and Response to Intravitreal Therapy in Diabetic Macular Edema.Journal of vitreoretinal diseases · 2026Article
- Scan Density Matters: Reproducibility of AI-Derived OCT Biomarkers in Diabetic Macular Edema.Translational vision science & technology · 2026Article
- Protective effects of luteolin on high glucose-induced ferroptosis in müller cells.International ophthalmology · 2025Article
- Efficacy and Safety of Faricimab in Diabetic Macular Edema: Real-World Outcomes in Treatment-Naïve and Previously Treated Eyes.Journal of clinical medicine · 2025Article
- Retinal Thickness Analysis Using Optical Coherence Tomography: Diagnostic and Monitoring Applications in Retinal Diseases.Diagnostics (Basel, Switzerland) · 2025Review
- Advances in OCT Angiography.Translational vision science & technology · 2025Review
- Artificial intelligence applications facilitate decision-making in cataract surgery for highly myopic patients.Frontiers in cell and developmental biology · 2025Article
- [OCT biomarkers in diabetic maculopathy and artificial intelligence].Die Ophthalmologie · 2024Review
- Article
- Visualizing features with wide-field volumetric OCT angiography.Optics express · 2024Article
- Applications of deep learning for detecting ophthalmic diseases with ultrawide-field fundus images.International journal of ophthalmology · 2024Review
- Single-shot OCT and OCT angiography for slab-specific detection of diabetic retinopathy.Biomedical optics express · 2023Article
- Artificial Intelligence-Based Quantification of Central Macular Fluid Volume and VA Prediction for Diabetic Macular Edema Using OCT Images.Ophthalmology and therapy · 2023Article
- Utility of En Face OCT for the Detection of Clinically Unsuspected Retinal Neovascularization in Patients with Diabetic Retinopathy.Ophthalmology. Retina · 2023Observational
- Characterisation of progression of macular oedema in the initial stages of diabetic retinopathy: a 3-year longitudinal study.Eye (London, England) · 2023Article
- Intravitreal injection of methotrexate in persistent diabetic macular edema: a 6-month study.Medical hypothesis, discovery & innovation ophthalmology journal · 2023Article
- Deep Capillary Nonperfusion on OCT Angiography Predicts Complications in Eyes with Referable Nonproliferative Diabetic Retinopathy.Ophthalmology. Retina · 2023Observational
- A Diabetic Retinopathy Classification Framework Based on Deep-Learning Analysis of OCT Angiography.Translational vision science & technology · 2022Article
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Authors and funding
9 authors at 1 institution in 1 country.
Funding
Abstract
importanceDiabetic macular edema (DME) is the predominant cause of visual impairment in patients with type 1 or 2 diabetes. Automated fluid volume measurements using optical coherence tomography (OCT) may improve the diagnostic accuracy of DME screening.
objectiveTo assess the diagnostic accuracy of an automated central macular fluid volume (CMFV) quantification using OCT for DME. DESIGN, SETTING, AND
participantsA cross-sectional observational study was conducted at a tertiary academic center among 215 patients with diabetes (1 eye each) enrolled from January 26, 2015, to December 23, 2019. All participants underwent comprehensive examinations, 6 × 6-mm macular structural OCT horizontal raster scans, and 6 × 6-mm macular OCT angiography volumetric scans. From January 1 to March 30, 2020, 2 retinal specialists reviewed the structural OCT scans independently and diagnosed DME if intraretinal or subretinal fluid was present. Diabetic macular edema was considered center involved if fluid was present within the central fovea (central 1-mm circle). A third retinal specialist arbitrated any discrepancy. The mean central subfield thickness (CST) within the central fovea was measured on structural OCT horizontal raster scans. A deep learning algorithm automatically quantified fluid volumes on 6 × 6-mm OCT angiography volumetric scans and within the central foveas (CMFV). MAIN OUTCOMES AND MEASURES: The area under the receiver operating characteristic curve (AUROC) and the sensitivity and specificity of CST and CMFV for DME diagnosis.
resultsWe enrolled 1 eye each of 215 patients with diabetes (117 women [54.4%]; mean [SD] age, 59.6 [12.4] years). Diabetic macular edema was present in 136 eyes; 93 cases of DME were center involved. The AUROC of CMFV for diagnosis of center-involved DME (0.907 [95% CI, 0.861-0.954]) was greater than the AUROC of CST (0.832 [95% CI, 0.775-0.889]; P = .02). With the specificity set at 95%, the sensitivity of CMFV for detection of center-involved DME (78.5% [95% CI, 68.8%-86.3%]) was higher than that of CST (53.8% [95% CI, 43.1%-64.2%]; P = .002). Center-involved DME cases not detected by CST but detected by CMFV were associated with a thinner CST (290.8 μm [95% CI, 282.3-299.3 μm] vs 369.4 μm [95% CI, 347.1-391.7 μm]; P < .001), higher proportion of previous macular laser treatment (11 of 28 [39.3%; 95% CI, 21.5%-59.4%] vs 12 of 65 [18.5%; 95% CI, 9.9%-30.0%]; P = .03), and female sex (20 of 28 [71.4%; 95% CI, 51.3%-86.8%] vs 31 of 65 [47.7%; 95% CI, 35.1%-60.5%]; P = .04). CONCLUSIONS AND RELEVANCE: These findings suggest that an automated CMFV is a more accurate diagnostic biomarker than CST for DME and may improve screening for DME.
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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.