ArticleDiabetes care2021
A Multitask Deep-Learning System to Classify Diabetic Macular Edema for Different Optical Coherence Tomography Devices: A Multicenter Analysis.
Article in Diabetes care, 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.
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Who cites it
21 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- In-depth analysis of research hotspots and emerging trends in AI for retinal diseases over the past decade.Frontiers in medicine · 2024Pooled it
- The diagnostic value of machine learning for the classification of malignant bone tumor: a systematic evaluation and meta-analysis.Frontiers in oncology · 2023Pooled it
- Automated Identification and Segmentation of Diabetic Macular Edema Subtypes Using Deep Learning.Translational vision science & technology · 2026Article
- Early Detection of Cystoid Macular Edema in Retinitis Pigmentosa Using Longitudinal Deep Learning Analysis of OCT Scans.Diagnostics (Basel, Switzerland) · 2025Article
- Artificial Intelligence in Diabetic Retinopathy and Diabetic Macular Edema: A Narrative Review.Bioengineering (Basel, Switzerland) · 2025Review
- Ensemble machine learning algorithm for anti-VEGF treatment efficacy prediction in diabetic macular edema.BMC ophthalmology · 2025Article
- Article
- A Deep Learning Network for Accurate Retinal Multidisease Diagnosis Using Multiview Fusion of En Face and B-Scan Images: A Multicenter Study.Translational vision science & technology · 2024Article
- Novel artificial intelligence algorithms for diabetic retinopathy and diabetic macular edema.Eye and vision (London, England) · 2024Review
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- The Present and Future of Artificial Intelligence-Based Medical Image in Diabetes Mellitus: Focus on Analytical Methods and Limitations of Clinical Use.Journal of Korean medical science · 2023Review
- Artificial intelligence for diabetic retinopathy in low-income and middle-income countries: a scoping review.BMJ open diabetes research & care · 2023Article
- Pathological Neurovascular Unit Mapping onto Multimodal Imaging in Diabetic Macular Edema.Medicina (Kaunas, Lithuania) · 2023Review
- Deep Learning in Optical Coherence Tomography Angiography: Current Progress, Challenges, and Future Directions.Diagnostics (Basel, Switzerland) · 2023Review
- Research progress in artificial intelligence assisted diabetic retinopathy diagnosis.International journal of ophthalmology · 2023Article
- Federated Learning in Ocular Imaging: Current Progress and Future Direction.Diagnostics (Basel, Switzerland) · 2022Review
- Review
- BO-ALLCNN: Bayesian-Based Optimized CNN for Acute Lymphoblastic Leukemia Detection in Microscopic Blood Smear Images.Sensors (Basel, Switzerland) · 2022Article
- Application of artificial intelligence models for detecting the pterygium that requires surgical treatment based on anterior segment images.Frontiers in neuroscience · 2022Article
- Three-Dimensional Multi-Task Deep Learning Model to Detect Glaucomatous Optic Neuropathy and Myopic Features From Optical Coherence Tomography Scans: A Retrospective Multi-Centre Study.Frontiers in medicine · 2022Article
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40 authors.
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Abstract
objectiveDiabetic macular edema (DME) is the primary cause of vision loss among individuals with diabetes mellitus (DM). We developed, validated, and tested a deep learning (DL) system for classifying DME using images from three common commercially available optical coherence tomography (OCT) devices. RESEARCH DESIGN AND
methodsWe trained and validated two versions of a multitask convolution neural network (CNN) to classify DME (center-involved DME [CI-DME], non-CI-DME, or absence of DME) using three-dimensional (3D) volume scans and 2D B-scans, respectively. For both 3D and 2D CNNs, we used the residual network (ResNet) as the backbone. For the 3D CNN, we used a 3D version of ResNet-34 with the last fully connected layer removed as the feature extraction module. A total of 73,746 OCT images were used for training and primary validation. External testing was performed using 26,981 images across seven independent data sets from Singapore, Hong Kong, the U.S., China, and Australia.
resultsIn classifying the presence or absence of DME, the DL system achieved area under the receiver operating characteristic curves (AUROCs) of 0.937 (95% CI 0.920-0.954), 0.958 (0.930-0.977), and 0.965 (0.948-0.977) for the primary data set obtained from CIRRUS, SPECTRALIS, and Triton OCTs, respectively, in addition to AUROCs >0.906 for the external data sets. For further classification of the CI-DME and non-CI-DME subgroups, the AUROCs were 0.968 (0.940-0.995), 0.951 (0.898-0.982), and 0.975 (0.947-0.991) for the primary data set and >0.894 for the external data sets.
conclusionsWe demonstrated excellent performance with a DL system for the automated classification of DME, highlighting its potential as a promising second-line screening tool for patients with DM, which may potentially create a more effective triaging mechanism to eye clinics.
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