ReviewClinical & experimental ophthalmology2023
Deep learning in optical coherence tomography: Where are the gaps?
Review in Clinical & experimental ophthalmology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
14 citing papers in PubMed.
- Current Trends in AI and Eye Disease Diagnostics.Ophthalmic & physiological optics : the journal of the British College of Ophthalmic Opticians (Optometrists) · 2026Review
- Deep learning-based prediction of one-year functional and structural outcomes in treatment-naïve DMO after anti-VEGF therapy.Eye (London, England) · 2026Article
- AI-driven multimodal retinal imaging for early detection and risk stratification of vascular and neurodegenerative diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2026Review
- Longitudinal analysis of iridian anterior segment OCT biomarkers in Fuchs uveitis syndrome and Posner-Schlossman syndrome.International ophthalmology · 2026Article
- Review
- Classification of Choroidal Neovascularization and Diabetic Macular Edema Based on Feature Extraction from Optical Coherence Tomography Images.Advances in experimental medicine and biology · 2026Article
- Impact of Optical Coherence Tomography (OCT) for Periodontitis Diagnostics: Current Overview and Advances.Dentistry journal · 2025Review
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- Construction of a predictive model for the efficacy of anti-VEGF therapy in macular edema patients based on OCT imaging: a retrospective study.Frontiers in medicine · 2025Article
- Review of cocaine-induced brain vascular and cellular function changes measuredNeurophotonics · 2025Review
- Synthetic polarization-sensitive optical coherence tomography using contrastive unpaired translation.Scientific reports · 2024Article
- High Prevalence of Artifacts in Optical Coherence Tomography With Adequate Signal Strength.Translational vision science & technology · 2024Article
- Comparison of Choroidal Vascular Index Measurements in Pachychoroid Pigment Epitheliopathy using Manual ImageJ-Based Niblack and Protocol-Standardized Otsu Thresholding.Journal of current ophthalmologyArticle
- Article
Corrections and comments
- Commented on by
Authors and funding
4 authors.
Funding
Abstract
Optical coherence tomography (OCT) is a non-invasive optical imaging modality, which provides rapid, high-resolution and cross-sectional morphology of macular area and optic nerve head for diagnosis and managing of different eye diseases. However, interpreting OCT images requires experts in both OCT images and eye diseases since many factors such as artefacts and concomitant diseases can affect the accuracy of quantitative measurements made by post-processing algorithms. Currently, there is a growing interest in applying deep learning (DL) methods to analyse OCT images automatically. This review summarises the trends in DL-based OCT image analysis in ophthalmology, discusses the current gaps, and provides potential research directions. DL in OCT analysis shows promising performance in several tasks: (1) layers and features segmentation and quantification; (2) disease classification; (3) disease progression and prognosis; and (4) referral triage level prediction. Different studies and trends in the development of DL-based OCT image analysis are described and the following challenges are identified and described: (1) public OCT data are scarce and scattered; (2) models show performance discrepancies in real-world settings; (3) models lack of transparency; (4) there is a lack of societal acceptance and regulatory standards; and (5) OCT is still not widely available in underprivileged areas. More work is needed to tackle the challenges and gaps, before DL is further applied in OCT image analysis for clinical use.
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.