ArticleScientific reports2022
Prediction of treatment outcome in neovascular age-related macular degeneration using a novel convolutional neural network.
Article in Scientific reports, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Artificial Intelligence-based characterization of therapeutic response in fluid types and volumes influencing retinal function in neovascular age-related macular degeneration.Scientific reports · 2026Trial
- A Rationale-Conditioned Image-Contrast Audit of OCT Dependence in Vision-Language Models for Anti-VEGF Treatment-Response Prediction.Bioengineering (Basel, Switzerland) · 2026Article
- Machine Learning to Predict Outcomes of Anti-VEGF Therapy in Neovascular Age-Related Macular Degeneration.Ophthalmology science · 2026Article
- Explainable multimodal AI and neuro-symbolic clinical decision support system for chronic eye disease management: a digital health implementation study.Frontiers in digital health · 2026Article
- Artificial intelligence for precision therapeutics in age-related macular degeneration: current advances, challenges, and future directions.Frontiers in artificial intelligence · 2026Review
- Predicting visual acuity using optical coherence tomography in patients with neovascular age-related macular degeneration.Scientific reports · 2025Article
- Explainable Artificial Intelligence Framework for Predicting Treatment Outcomes in Age-Related Macular Degeneration.Sensors (Basel, Switzerland) · 2025Article
- AI-Based Response Classification After Anti-VEGF Loading in Neovascular Age-Related Macular Degeneration.Diagnostics (Basel, Switzerland) · 2025Article
- Age-Related Macular Degeneration: Cellular and Molecular Signaling Mechanisms.International journal of molecular sciences · 2025Review
- Application of Artificial Intelligence Models to Predict the Onset or Recurrence of Neovascular Age-Related Macular Degeneration.Pharmaceuticals (Basel, Switzerland) · 2024Review
- A Performance Evaluation of Convolutional Neural Network Architectures for Pterygium Detection in Anterior Segment Eye Images.Diagnostics (Basel, Switzerland) · 2024Article
- Associations of presenting visual acuity with morphological changes on OCT in neovascular age-related macular degeneration: PRECISE Study Report 2.Eye (London, England) · 2024Article
- Artificial intelligence in individualized retinal disease management.International journal of ophthalmology · 2024Review
- Deep learning in optical coherence tomography: Where are the gaps?Clinical & experimental ophthalmology · 2023Review
- Prediction of anti-vascular endothelial growth factor agent-specific treatment outcomes in neovascular age-related macular degeneration using a generative adversarial network.Scientific reports · 2023Article
- Research progress on diagnosing retinal vascular diseases based on artificial intelligence and fundus images.Frontiers in cell and developmental biology · 2023Review
- An overview of artificial intelligence in diabetic retinopathy and other ocular diseases.Frontiers in public health · 2022Review
- Artificial intelligence in age-related macular degeneration: Advancing diagnosis, prognosis, and treatment.Survey of ophthalmologyReview
- Application of Artificial Intelligence in Ophthalmology: An Updated Comprehensive Review.Journal of ophthalmic & vision researchReview
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Authors and funding
9 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
While prognosis and risk of progression are crucial in developing precise therapeutic strategy in neovascular age-related macular degeneration (nAMD), limited predictive tools are available. We proposed a novel deep convolutional neural network that enables feature extraction through image and non-image data integration to seize imperative information and achieve highly accurate outcome prediction. The Heterogeneous Data Fusion Net (HDF-Net) was designed to predict visual acuity (VA) outcome (improvement ≥ 2 line or not) at 12th months after anti-VEGF treatment. A set of pre-treatment optical coherence tomography (OCT) image and non-image demographic features were employed as input data and the corresponding 12th-month post-treatment VA as the target data to train, validate, and test the HDF-Net. This newly designed HDF-Net demonstrated an AUC of 0.989 (95% CI 0.970-0.999), accuracy of 0.936 [95% confidence interval (CI) 0.889-0.964], sensitivity of 0.933 (95% CI 0.841-0.974), and specificity of 0.938 (95% CI 0.877-0.969). By simulating the clinical decision process with mixed pre-treatment information from raw OCT images and numeric data, HDF-Net demonstrated promising performance in predicting individualized treatment outcome. The results highlight the potential of deep learning to simultaneously process a broad range of clinical data to weigh and leverage the complete information of the patient. This novel approach is an important step toward real-world personalized therapeutic strategy for typical nAMD.
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