ReviewBioengineering (Basel, Switzerland)2023
On Machine Learning in Clinical Interpretation of Retinal Diseases Using OCT Images.
Review in Bioengineering (Basel, Switzerland), 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 11 papers.
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
11 citing papers in PubMed, 23 citations in OpenAlex.
- Image-Quality-Aware Multimodal Artificial Intelligence for Automated Structured OCT Report Generation in Glaucoma Evaluation.Ophthalmology science · 2026Article
- Current Applications of Artificial Intelligence in Neuro-Ophthalmic Imaging: A Narrative Approach.Medical sciences (Basel, Switzerland) · 2026Review
- Glaucoma Detection and Structured OCT Report Generation via a Fine-tuned Multimodal Large Language Model.ArXiv · 2025Article
- AI-assisted ophthalmic imaging for early detection of neurodegenerative diseases.International journal of emergency medicine · 2025Review
- Automated Foveal Avascular Zone Segmentation in Optical Coherence Tomography Angiography Across Multiple Eye Diseases Using Knowledge Distillation.Bioengineering (Basel, Switzerland) · 2025Article
- Benchmarking diffusion models against state-of-the-art architectures for OCT fluid biomarker segmentation.PloS one · 2025Article
- Precision Segmentation of Subretinal Fluids in OCT Using Multiscale Attention-Based U-Net Architecture.Bioengineering (Basel, Switzerland) · 2024Article
- Machine Learning and Optical-Coherence-Tomography-Derived Radiomics Analysis to Predict the Postoperative Anatomical Outcome of Full-Thickness Macular Hole.Bioengineering (Basel, Switzerland) · 2024Article
- Exploring Publicly Accessible Optical Coherence Tomography Datasets: A Comprehensive Overview.Diagnostics (Basel, Switzerland) · 2024Review
- Advancing Ocular Imaging: A Hybrid Attention Mechanism-Based U-Net Model for Precise Segmentation of Sub-Retinal Layers in OCT Images.Bioengineering (Basel, Switzerland) · 2024Article
- Multi-Stage Classification of Retinal OCT Using Multi-Scale Ensemble Deep Architecture.Bioengineering (Basel, Switzerland) · 2023Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors at 1 institution in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Optical coherence tomography (OCT) is a noninvasive imaging technique that provides high-resolution cross-sectional retina images, enabling ophthalmologists to gather crucial information for diagnosing various retinal diseases. Despite its benefits, manual analysis of OCT images is time-consuming and heavily dependent on the personal experience of the analyst. This paper focuses on using machine learning to analyse OCT images in the clinical interpretation of retinal diseases. The complexity of understanding the biomarkers present in OCT images has been a challenge for many researchers, particularly those from nonclinical disciplines. This paper aims to provide an overview of the current state-of-the-art OCT image processing techniques, including image denoising and layer segmentation. It also highlights the potential of machine learning algorithms to automate the analysis of OCT images, reducing time consumption and improving diagnostic accuracy. Using machine learning in OCT image analysis can mitigate the limitations of manual analysis methods and provide a more reliable and objective approach to diagnosing retinal diseases. This paper will be of interest to ophthalmologists, researchers, and data scientists working in the field of retinal disease diagnosis and machine learning. By presenting the latest advancements in OCT image analysis using machine learning, this paper will contribute to the ongoing efforts to improve the diagnostic accuracy of retinal diseases.
Indexed as
Identifiers
What OpenQuestion holds
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.