ArticleJournal of healthcare engineering2019
Glaucoma Diagnosis with Machine Learning Based on Optical Coherence Tomography and Color Fundus Images.
Article in Journal of healthcare engineering, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 48 papers, 1 of them a synthesis that pooled it.
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Who cites it
48 citing papers in PubMed, 1 synthesis or guideline pooled it, 195 citations in OpenAlex.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Large Language Models Approximate Inter-Expert Agreement in Glaucoma Suspect and Glaucoma Classification from Multimodal Data.Ophthalmology science · 2026Article
- Review
- The aging eye: navigating molecular mechanisms and innovative interventions.Biogerontology · 2026Review
- Explainable AI-driven diagnosis model for early glaucoma detection using grey-wolf optimized extreme learning machine approach.PLoS computational biology · 2026Article
- ARTNet: Adaptive channel-wise and Region-aware Transformer Network for diabetic retinopathy segmentation and classification.Frontiers in artificial intelligence · 2026Article
- Applications of Optical Coherence Tomography in Optic Nerve Head Diseases: A Narrative Review.Diagnostics (Basel, Switzerland) · 2025Review
- ISOSNet: a unified framework for cone photoreceptor detection and inner segment and outer segment length measurement from AO-OCT B-scans.Biomedical optics express · 2025Article
- AFTG-Net: A Deep Attention-based Fusion Framework of Topological and Gradient Features for Pathological Image Analysis.Research square · 2025Article
- The role of artificial intelligence in the diagnosis of diabetic retinopathy through retinal lesion features: a narrative review.Quantitative imaging in medicine and surgery · 2025Review
- Diagnosis of early glaucoma likely combined with high myopia by integrating OCT thickness map and standard automated and Pulsar perimetries.Scientific reports · 2025Article
- Multiple model visual feature embedding and selection method for an efficient oncular disease classification.Scientific reports · 2025Article
- Novel Approaches for the Early Detection of Glaucoma Using Artificial Intelligence.Life (Basel, Switzerland) · 2024Review
- Multimodality Fusion Strategies in Eye Disease Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Multi-label deep learning for comprehensive optic nerve head segmentation through data of fundus images.Heliyon · 2024Article
- A hybrid framework for glaucoma detection through federated machine learning and deep learning models.BMC medical informatics and decision making · 2024Article
- Transforming glaucoma diagnosis: transformers at the forefront.Frontiers in artificial intelligence · 2024Article
- Multimode sensing based on optical microcavities.Frontiers of optoelectronics · 2023Review
- Artifact-Tolerant Clustering-Guided Contrastive Embedding Learning for Ophthalmic Images in Glaucoma.IEEE journal of biomedical and health informatics · 2023Article
- Optical Coherence Tomography Image Classification Using Hybrid Deep Learning and Ant Colony Optimization.Sensors (Basel, Switzerland) · 2023Article
Corrections and comments
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Authors and funding
10 authors at 2 institutions in 1 country.
Funding
No grant is acknowledged in the PubMed record.
Abstract
This study aimed to develop a machine learning-based algorithm for glaucoma diagnosis in patients with open-angle glaucoma, based on three-dimensional optical coherence tomography (OCT) data and color fundus images. In this study, 208 glaucomatous and 149 healthy eyes were enrolled, and color fundus images and volumetric OCT data from the optic disc and macular area of these eyes were captured with a spectral-domain OCT (3D OCT-2000, Topcon). Thickness and deviation maps were created with a segmentation algorithm. Transfer learning of convolutional neural network (CNN) was used with the following types of input images: (1) fundus image of optic disc in grayscale format, (2) disc retinal nerve fiber layer (RNFL) thickness map, (3) macular ganglion cell complex (GCC) thickness map, (4) disc RNFL deviation map, and (5) macular GCC deviation map. Data augmentation and dropout were performed to train the CNN. For combining the results from each CNN model, a random forest (RF) was trained to classify the disc fundus images of healthy and glaucomatous eyes using feature vector representation of each input image, removing the second fully connected layer. The area under receiver operating characteristic curve (AUC) of a 10-fold cross validation (CV) was used to evaluate the models. The 10-fold CV AUCs of the CNNs were 0.940 for color fundus images, 0.942 for RNFL thickness maps, 0.944 for macular GCC thickness maps, 0.949 for disc RNFL deviation maps, and 0.952 for macular GCC deviation maps. The RF combining the five separate CNN models improved the 10-fold CV AUC to 0.963. Therefore, the machine learning system described here can accurately differentiate between healthy and glaucomatous subjects based on their extracted images from OCT data and color fundus images. This system should help to improve the diagnostic accuracy in glaucoma.
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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.