ArticleJMIR medical informatics2021
A Multimodal Imaging-Based Deep Learning Model for Detecting Treatment-Requiring Retinal Vascular Diseases: Model Development and Validation Study.
Article in JMIR medical informatics, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 32 papers, 4 of them syntheses that pooled it.
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Who cites it
32 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Vitreoretinal disease detection using artificial intelligence: a systematic review and meta-analysis.International ophthalmology · 2026Pooled it
- Multimodal artificial intelligence in retinal vascular and neovascular macular diseases: a systematic review of diagnostic and prognostic applications.BMC ophthalmology · 2025Pooled it
- Artificial intelligence in pathologic myopia: a review of clinical research studies.Frontiers in medicine · 2025Pooled it
- Artificial intelligence for diagnosing exudative age-related macular degeneration.The Cochrane database of systematic reviews · 2024Pooled it
- A systematic review on deep learning techniques for diabetic retinopathy classification in retinal fundus images.iScience · 2026Article
- Advanced CNN Deep Learning Model for Diabetic Retinopathy Classification.Journal of biomedical physics & engineering · 2026Article
- A Comparative Analysis of Deep Convolutional Networks for Automated Diagnosis of Retinal Detachment in Dogs.Veterinary ophthalmology · 2026Article
- Bridging modalities with AI: a review of AI advances in multimodal biomedical imaging.Communications engineering · 2026Review
- Artificial Intelligence-Based Medical Devices for Diabetic Retinopathy Screening in the European Union.Ophthalmology and therapy · 2026Review
- Multimodal Deep Learning for Diabetic Retinopathy: A Survey.Journal of ophthalmology · 2026Review
- Review
- A Vision-Language-Guided Multimodal Fusion Network for Glottic Carcinoma Early Diagnosis: Model Development and Validation Study.JMIR medical informatics · 2025Article
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- 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
- Multimodal machine learning enables AI chatbot to diagnose ophthalmic diseases and provide high-quality medical responses.NPJ digital medicine · 2025Article
- Diagnostic performance and generalizability of deep learning for multiple retinal diseases using bimodal imaging of fundus photography and optical coherence tomography.Frontiers in cell and developmental biology · 2025Article
- A Novel Foundation Model-Based Framework for Multimodal Retinal Age Prediction.IEEE journal of translational engineering in health and medicine · 2025Article
- Multimodality Fusion Strategies in Eye Disease Diagnosis.Journal of imaging informatics in medicine · 2024Article
- Fundus Image Deep Learning Study to Explore the Association of Retinal Morphology with Age-Related Macular Degeneration Polygenic Risk Score.Biomedicines · 2024Article
- Ocular biomarkers: useful incidental findings by deep learning algorithms in fundus photographs.Eye (London, England) · 2024Article
Corrections and comments
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10 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundRetinal vascular diseases, including diabetic macular edema (DME), neovascular age-related macular degeneration (nAMD), myopic choroidal neovascularization (mCNV), and branch and central retinal vein occlusion (BRVO/CRVO), are considered vision-threatening eye diseases. However, accurate diagnosis depends on multimodal imaging and the expertise of retinal ophthalmologists.
objectiveThe aim of this study was to develop a deep learning model to detect treatment-requiring retinal vascular diseases using multimodal imaging.
methodsThis retrospective study enrolled participants with multimodal ophthalmic imaging data from 3 hospitals in Taiwan from 2013 to 2019. Eye-related images were used, including those obtained through retinal fundus photography, optical coherence tomography (OCT), and fluorescein angiography with or without indocyanine green angiography (FA/ICGA). A deep learning model was constructed for detecting DME, nAMD, mCNV, BRVO, and CRVO and identifying treatment-requiring diseases. Model performance was evaluated and is presented as the area under the curve (AUC) for each receiver operating characteristic curve.
resultsA total of 2992 eyes of 2185 patients were studied, with 239, 1209, 1008, 211, 189, and 136 eyes in the control, DME, nAMD, mCNV, BRVO, and CRVO groups, respectively. Among them, 1898 eyes required treatment. The eyes were divided into training, validation, and testing groups in a 5:1:1 ratio. In total, 5117 retinal fundus photos, 9316 OCT images, and 20,922 FA/ICGA images were used. The AUCs for detecting mCNV, DME, nAMD, BRVO, and CRVO were 0.996, 0.995, 0.990, 0.959, and 0.988, respectively. The AUC for detecting treatment-requiring diseases was 0.969. From the heat maps, we observed that the model could identify retinal vascular diseases.
conclusionsOur study developed a deep learning model to detect retinal diseases using multimodal ophthalmic imaging. Furthermore, the model demonstrated good performance in detecting treatment-requiring retinal diseases.
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