ArticleBiomedical optics express2018
Deep learning approach for the detection and quantification of intraretinal cystoid fluid in multivendor optical coherence tomography.
Article in Biomedical optics express, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 1 of them a synthesis that pooled it.
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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.
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Who cites it
51 citing papers in PubMed, 1 synthesis or guideline pooled it.
- Systematic Review of Retinal Blood Vessels Segmentation Based on AI-driven Technique.Journal of imaging informatics in medicine · 2024Pooled it
- Expanding insights from the VISTA phase III trial: longitudinal comparative assessment of ellipsoid zone integrity and volumetric fluid dynamics.Scientific reports · 2025Trial
- Longitudinal Higher-Order OCT Assessment of Quantitative Fluid Dynamics and the Total Retinal Fluid Index in Neovascular AMD.Translational vision science & technology · 2021Trial
- Retinal Specialist versus Artificial Intelligence Detection of Retinal Fluid from OCT: Age-Related Eye Disease Study 2: 10-Year Follow-On Study.Ophthalmology · 2021Trial
- Beyond Performance Metrics: Automatic Deep Learning Retinal OCT Analysis Reproduces Clinical Trial Outcome.Ophthalmology · 2020Trial
- Review
- Validation of Inter-Reader Agreement/Consistency for Quantification of Ellipsoid Zone Integrity and Sub-RPE Compartmental Features Across Retinal Diseases.Diagnostics (Basel, Switzerland) · 2024Article
- Artificial Intelligence and Advanced Technology in Glaucoma: A Review.Journal of personalized medicine · 2024Review
- OCTDL: Optical Coherence Tomography Dataset for Image-Based Deep Learning Methods.Scientific data · 2024Article
- Diabetic Macular Edema Optical Coherence Tomography Biomarkers Detected with EfficientNetV2B1 and ConvNeXt.Diagnostics (Basel, Switzerland) · 2023Article
- Fuzzy Logic-Based System for Identifying the Severity of Diabetic Macular Edema from OCT B-Scan Images Using DRIL, HRF, and Cystoids.Diagnostics (Basel, Switzerland) · 2023Article
- Optimal Deep Learning Architecture for Automated Segmentation of Cysts in OCT Images Using X-Let Transforms.Diagnostics (Basel, Switzerland) · 2023Article
- On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.Bioengineering (Basel, Switzerland) · 2023Review
- Cyst identification in retinal optical coherence tomography images using hidden Markov model.Scientific reports · 2023Article
- A bibliometric analysis of artificial intelligence applications in macular edema: exploring research hotspots and Frontiers.Frontiers in cell and developmental biology · 2023Article
- AI-Aided Disease Prediction in Visualized Medicine.Advances in experimental medicine and biology · 2023Article
- Artificial intelligence-assisted diagnosis of ocular surface diseases.Frontiers in cell and developmental biology · 2023Review
- Fully-Automatic 3D Intuitive Visualization of Age-Related Macular Degeneration Fluid Accumulations in OCT Cubes.Journal of digital imaging · 2022Article
- Trustworthy AI: Closing the gap between development and integration of AI systems in ophthalmic practice.Progress in retinal and eye research · 2022Review
- Recent Advanced Deep Learning Architectures for Retinal Fluid Segmentation on Optical Coherence Tomography Images.Sensors (Basel, Switzerland) · 2022Review
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
We developed a deep learning algorithm for the automatic segmentation and quantification of intraretinal cystoid fluid (IRC) in spectral domain optical coherence tomography (SD-OCT) volumes independent of the device used for acquisition. A cascade of neural networks was introduced to include prior information on the retinal anatomy, boosting performance significantly. The proposed algorithm approached human performance reaching an overall Dice coefficient of 0.754 ± 0.136 and an intraclass correlation coefficient of 0.936, for the task of IRC segmentation and quantification, respectively. The proposed method allows for fast quantitative IRC volume measurements that can be used to improve patient care, reduce costs, and allow fast and reliable analysis in large population studies.
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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.