ReviewBMC medical informatics and decision making2014
A survey on computer aided diagnosis for ocular diseases.
Review in BMC medical informatics and decision making, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 22 papers.
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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.
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Who cites it
22 citing papers in PubMed.
- Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach.PLoS computational biology · 2026Article
- Digital slit lamp derived parameters in shallow anterior chamber screening.Scientific reports · 2025Article
- ODDM: Integration of SMOTE Tomek with Deep Learning on Imbalanced Color Fundus Images for Classification of Several Ocular Diseases.Journal of imaging · 2025Article
- A Comprehensive Review of AI Diagnosis Strategies for Age-Related Macular Degeneration (AMD).Bioengineering (Basel, Switzerland) · 2024Review
- Computer-Aided Discrimination of Glaucoma Patients from Healthy Subjects Using the RETeval Portable Device.Diagnostics (Basel, Switzerland) · 2024Article
- Identifying and Exploring the Impact Factors for Intraocular Pressure Prediction in Myopic Children with Atropine Control Utilizing Multivariate Adaptive Regression Splines.Journal of personalized medicine · 2024Article
- Biomedical Data Annotation: An OCT Imaging Case Study.Journal of ophthalmology · 2023Article
- Classification of breast cancer histology images using MSMV-PFENet.Scientific reports · 2022Article
- Evaluations of Deep Learning Approaches for Glaucoma Screening Using Retinal Images from Mobile Device.Sensors (Basel, Switzerland) · 2022Article
- Literature Review on Artificial Intelligence Methods for Glaucoma Screening, Segmentation, and Classification.Journal of imaging · 2022Review
- Artificial intelligence applied to ophthalmology and optometry: A citation network analysis.Journal of optometry · 2022Article
- Automatic classification of heterogeneous slit-illumination images using an ensemble of cost-sensitive convolutional neural networks.Annals of translational medicine · 2021Article
- Implementation of a Framework for Healthy and Diabetic Retinopathy Retinal Image Recognition.Scientifica · 2020Article
- Application of machine learning in ophthalmic imaging modalities.Eye and vision (London, England) · 2020Review
- A Random Forest classifier-based approach in the detection of abnormalities in the retina.Medical & biological engineering & computing · 2019Article
- An efficient optic cup segmentation method decreasing the influences of blood vessels.Biomedical engineering online · 2018Article
- Computer-Aided Diagnosis of Anterior Segment Eye Abnormalities using Visible Wavelength Image Analysis Based Machine Learning.Journal of medical systems · 2018Article
- Predicting the progression of ophthalmic disease based on slit-lamp images using a deep temporal sequence network.PloS one · 2018Article
- Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.Biomedical engineering online · 2017Article
- Comparative analysis of image classification methods for automatic diagnosis of ophthalmic images.Scientific reports · 2017Article
Corrections and comments
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundComputer Aided Diagnosis (CAD), which can automate the detection process for ocular diseases, has attracted extensive attention from clinicians and researchers alike. It not only alleviates the burden on the clinicians by providing objective opinion with valuable insights, but also offers early detection and easy access for patients.
methodWe review ocular CAD methodologies for various data types. For each data type, we investigate the databases and the algorithms to detect different ocular diseases. Their advantages and shortcomings are analyzed and discussed.
resultWe have studied three types of data (i.e., clinical, genetic and imaging) that have been commonly used in existing methods for CAD. The recent developments in methods used in CAD of ocular diseases (such as Diabetic Retinopathy, Glaucoma, Age-related Macular Degeneration and Pathological Myopia) are investigated and summarized comprehensively.
conclusionWhile CAD for ocular diseases has shown considerable progress over the past years, the clinical importance of fully automatic CAD systems which are able to embed clinical knowledge and integrate heterogeneous data sources still show great potential for future breakthrough.
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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.