ArticleIEEE transactions on bio-medical engineering2021
Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images.
Article in IEEE transactions on bio-medical engineering, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers, 2 of them syntheses that pooled it.
What it found
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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.
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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Artificial intelligence in glaucoma: opportunities, challenges, and future directions.Biomedical engineering online · 2023Pooled it
- Deep learning and optical coherence tomography in glaucoma: Bridging the diagnostic gap on structural imaging.Frontiers in ophthalmology · 2022Pooled it
- A Lightweight CNN for Multiclass Retinal Disease Screening with Explainable AI.Journal of imaging · 2025Article
- A scoping review of advancements in machine learning for glaucoma: current trends and future direction.Frontiers in medicine · 2025Article
- Influence of training and expertise on deep neural network attention and human attention during a medical image classification task.Journal of vision · 2024Article
- Advancing Glaucoma Care: Integrating Artificial Intelligence in Diagnosis, Management, and Progression Detection.Bioengineering (Basel, Switzerland) · 2024Review
- Computational methods in glaucoma research: Current status and future outlook.Molecular aspects of medicine · 2023Review
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Retinal Changes in Parkinson's Disease: A Non-invasive Biomarker for Early Diagnosis.Cellular and molecular neurobiology · 2023Review
- Automated analysis of vessel morphometry in retinal images from a Danish high street optician setting.PloS one · 2023Article
- Extracting decision-making features from the unstructured eye movements of clinicians on glaucoma OCT reports and developing AI models to classify expertise.Frontiers in medicine · 2023Article
- Validating Automatic Concept-Based Explanations for AI-Based Digital Histopathology.Sensors (Basel, Switzerland) · 2022Article
- Rationale and Development of an OCT-Based Method for Detection of Glaucomatous Optic Neuropathy.Journal of glaucoma · 2022Article
- The OCT RNFL Probability Map and Artifacts Resembling Glaucomatous Damage.Translational vision science & technology · 2022Article
- Identifying the Retinal Layers Linked to Human Contrast Sensitivity Via Deep Learning.Investigative ophthalmology & visual science · 2022Article
- Automatic Quantification of Anterior Lamina Cribrosa Structures in Optical Coherence Tomography Using a Two-Stage CNN Framework.Sensors (Basel, Switzerland) · 2021Article
- Robust and Interpretable Convolutional Neural Networks to Detect Glaucoma in Optical Coherence Tomography Images.IEEE transactions on bio-medical engineering · 2021Article
- Strategies to Improve Convolutional Neural Network Generalizability and Reference Standards for Glaucoma Detection From OCT Scans.Translational vision science & technology · 2021Article
- Application of artificial intelligence in glaucoma care: An updated review.Taiwan journal of ophthalmologyReview
- Deep Learning-Assisted Detection of Glaucoma Progression in Spectral-Domain OCT.Ophthalmology. GlaucomaArticle
Corrections and comments
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Authors and funding
4 authors.
Funding
Abstract
Recent studies suggest that deep learning systems can now achieve performance on par with medical experts in diagnosis of disease. A prime example is in the field of ophthalmology, where convolutional neural networks (CNNs) have been used to detect retinal and ocular diseases. However, this type of artificial intelligence (AI) has yet to be adopted clinically due to questions regarding robustness of the algorithms to datasets collected at new clinical sites and a lack of explainability of AI-based predictions, especially relative to those of human expert counterparts. In this work, we develop CNN architectures that demonstrate robust detection of glaucoma in optical coherence tomography (OCT) images and test with concept activation vectors (TCAVs) to infer what image concepts CNNs use to generate predictions. Furthermore, we compare TCAV results to eye fixations of clinicians, to identify common decision-making features used by both AI and human experts. We find that employing fine-tuned transfer learning and CNN ensemble learning create end-to-end deep learning models with superior robustness compared to previously reported hybrid deep-learning/machine-learning models, and TCAV/eye-fixation comparison suggests the importance of three OCT report sub-images that are consistent with areas of interest fixated upon by OCT experts to detect glaucoma. The pipeline described here for evaluating CNN robustness and validating interpretable image concepts used by CNNs with eye movements of experts has the potential to help standardize the acceptance of new AI tools for use in the clinic.
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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.