ReviewTranslational vision science & technology2020
Methodological Challenges of Deep Learning in Optical Coherence Tomography for Retinal Diseases: A Review.
Review in Translational vision science & technology, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 39 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
39 citing papers in PubMed.
- Review
- Neurosymbolic AI Framework for Explainable Retinal Disease Classification From OCT Images.Translational vision science & technology · 2026Article
- Long-term outcomes of anti-VEGF therapy during cataract surgery in eyes with diabetic macular edema: a five-year retrospective cohort study.International journal of ophthalmology · 2026Article
- PAIR: Evaluating the Limits of Agreement Among Non-Retinal Specialist Using PathFinder Artificial Intelligence Tool for Retinal Disease Referrals: A Prospective Observational Study.Clinical ophthalmology (Auckland, N.Z.) · 2026Article
- The march to harmonized imaging standards for retinal imaging.Progress in retinal and eye research · 2025Review
- Identifying Retinal Features Using a Self-Configuring CNN for Clinical Intervention.Investigative ophthalmology & visual science · 2025Article
- Publicly available imaging datasets for age-related macular degeneration: Evaluation according to the Findable, Accessible, Interoperable, Reusable (FAIR) principles.Experimental eye research · 2025Review
- A review on diagnostic assessments of tracheal stenosis.Biomedical engineering online · 2025Review
- Association Between Dementia and Optical Coherence Tomography Scan Quality.Aging and disease · 2025Article
- Robotics and optical coherence tomography: current works and future perspectives [Invited].Biomedical optics express · 2025Review
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- Evaluating deep learning models for classifying OCT images with limited data and noisy labels.Scientific reports · 2024Article
- Hybrid deep learning models for the screening of Diabetic Macular Edema in optical coherence tomography volumes.Scientific reports · 2024Article
- An automated process for bulk downloading optical coherence tomography scans.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2024Article
- RobOCTNet: Robotics and Deep Learning for Referable Posterior Segment Pathology Detection in an Emergency Department Population.Translational vision science & technology · 2024Article
- Inter-rater reliability in labeling quality and pathological features of retinal OCT scans: A customized annotation software approach.PloS one · 2024Article
- OCT-based deep-learning models for the identification of retinal key signs.Scientific reports · 2023Article
- Automated, high-throughput quantification of EGFP-expressing neutrophils in zebrafish by machine learning and a highly-parallelized microscope.bioRxiv : the preprint server for biology · 2023Article
- The Predictive Capabilities of Artificial Intelligence-Based OCT Analysis for Age-Related Macular Degeneration Progression-A Systematic Review.Diagnostics (Basel, Switzerland) · 2023Review
- Annotation-efficient learning for OCT segmentation.Biomedical optics express · 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
4 authors.
Funding
Abstract
Artificial intelligence (AI)-based automated classification and segmentation of optical coherence tomography (OCT) features have become increasingly popular. However, its 3-dimensional volumetric nature has made developing an algorithm that generalizes across all patient populations and OCT devices challenging. Several recent studies have reported high diagnostic performances of AI models; however, significant methodological challenges still exist in applying these models in real-world clinical practice. Lack of large-image datasets from multiple OCT devices, nonstandardized imaging or post-processing protocols between devices, limited graphics processing unit capabilities for exploiting 3-dimensional features, and inconsistency in the reporting metrics are major hurdles in enabling AI for OCT analyses. We discuss these issues and present possible solutions.
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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.