ArticleScientific reports2023
OCT-based deep-learning models for the identification of retinal key signs.
Article in Scientific reports, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 25 papers, 1 of them a synthesis that pooled it.
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Who cites it
25 citing papers in PubMed, 1 synthesis or guideline pooled it, 44 citations in OpenAlex.
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- Label-free diagnosis across the thyroid nodule pathology spectrum using deep learning-enabled optical coherence tomography.Biomedical optics express · 2026Article
- Utilizing optical coherence tomography and machine learning to identify vision abnormalities in pediatric neurofibromatosis type 1 patients.Scientific reports · 2026Article
- Neurosymbolic AI Framework for Explainable Retinal Disease Classification From OCT Images.Translational vision science & technology · 2026Article
- Leveraging CNN and Transfer Learning With EfficientNet for Enhanced Optical Coherence Tomography (OCT).International journal of biomedical imaging · 2026Article
- Multi-OCT-SelfNet: integrating self-supervised learning with multi-source data fusion for enhanced multi-class retinal disease classification.Frontiers in systems biology · 2026Article
- Early Detection of Cystoid Macular Edema in Retinitis Pigmentosa Using Longitudinal Deep Learning Analysis of OCT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Deep Learning for Autonomous Surgical Guidance Using 3-Dimensional Images From Forward-Viewing Endoscopic Optical Coherence Tomography.Journal of biophotonics · 2025Article
- NeoNet: A Novel Deep Learning Model for Retinal Disease Diagnosis and Localization.Sensors (Basel, Switzerland) · 2025Article
- From Pathophysiology to Innovative Therapies in Eye Diseases: A Brief Overview.International journal of molecular sciences · 2025Review
- Diagnostic report generation for macular diseases by natural language processing algorithms.The British journal of ophthalmology · 2025Article
- Controversies, consensuses, and guidelines on macular hole surgery by the Asia-Pacific Vitreo-retina Society (APVRS) and the Asia-Pacific Academy of Professors in Ophthalmology (AAPPO).Eye and vision (London, England) · 2025Review
- Deep Learning Model for Automated Classification of Macular Neovascularization Subtypes in AMD.Investigative ophthalmology & visual science · 2025Article
- Recent Optical Coherence Tomography (OCT) Innovations for Increased Accessibility and Remote Surveillance.Bioengineering (Basel, Switzerland) · 2025Review
- Association Between Dementia and Optical Coherence Tomography Scan Quality.Aging and disease · 2025Article
- Deep Learning Approach Predicts Longitudinal Retinal Nerve Fiber Layer Thickness Changes.Bioengineering (Basel, Switzerland) · 2025Article
- A Survey on Optical Coherence Tomography-Technology and Application.Bioengineering (Basel, Switzerland) · 2025Review
- OCT-SelfNet: a self-supervised framework with multi-source datasets for generalized retinal disease detection.Frontiers in big data · 2025Article
- Evaluating deep learning models for classifying OCT images with limited data and noisy labels.Scientific reports · 2024Article
- Detection of Disease Features on Retinal OCT Scans Using RETFound.Bioengineering (Basel, Switzerland) · 2024Article
Corrections and comments
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Authors and funding
7 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
A new system based on binary Deep Learning (DL) convolutional neural networks has been developed to recognize specific retinal abnormality signs on Optical Coherence Tomography (OCT) images useful for clinical practice. Images from the local hospital database were retrospectively selected from 2017 to 2022. Images were labeled by two retinal specialists and included central fovea cross-section OCTs. Nine models were developed using the Visual Geometry Group 16 architecture to distinguish healthy versus abnormal retinas and to identify eight different retinal abnormality signs. A total of 21,500 OCT images were screened, and 10,770 central fovea cross-section OCTs were included in the study. The system achieved high accuracy in identifying healthy retinas and specific pathological signs, ranging from 93 to 99%. Accurately detecting abnormal retinal signs from OCT images is crucial for patient care. This study aimed to identify specific signs related to retinal pathologies, aiding ophthalmologists in diagnosis. The high-accuracy system identified healthy retinas and pathological signs, making it a useful diagnostic aid. Labelled OCT images remain a challenge, but our approach reduces dataset creation time and shows DL models' potential to improve ocular pathology diagnosis and clinical decision-making.
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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.