ArticleNPJ digital medicine2024
Translating color fundus photography to indocyanine green angiography using deep-learning for age-related macular degeneration screening.
Article in NPJ digital medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers.
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Who cites it
24 citing papers in PubMed, 29 citations in OpenAlex.
- TPA-ConvNeXt: Trigonometric Phase Attention for Robust Retinal Disease Classification Across Fundus and OCT.Bioengineering (Basel, Switzerland) · 2026Article
- Fast nanoDSF Tear Fluid Profiling: Toward Diagnosis of Age-Related Macular Degeneration.Life (Basel, Switzerland) · 2026Article
- Fundus to fluorescein angiography video generation as a retinal generative foundation model.NPJ digital medicine · 2026Article
- Agentic AI in global health equity for high altitude populations.NPJ digital medicine · 2026Review
- Improved method for optical coherence tomography angiography: from reconstruction to clinical indicator quantification.Journal of biomedical optics · 2026Article
- Article
- Angiography-free diagnosis of retinal diseases via interpretable multi-modal learning.NPJ digital medicine · 2026Article
- Boosting foundation models for rare eye disease diagnosis via a multimodal text-to-image generative framework.NPJ digital medicine · 2026Article
- Real-time reconstruction of 3D bone models via very-low-dose protocols.NPJ digital medicine · 2026Article
- EvoApneaFormer: an IoT and prognostic evolutionary deep learning-based framework for real-time multi-event sleep apnea disorder detection and remote monitoring.Frontiers in bioengineering and biotechnology · 2026Article
- Generative Artificial Intelligence for Retinal Image Translation to Improve Glaucoma Screening With Deep Learning.Translational vision science & technology · 2025Article
- Choroidal Vascular Fingerprints From Indocyanine Green Angiography Unveil Chorioretinal Disease State.Investigative ophthalmology & visual science · 2025Article
- Applications of generative adversarial networks in the diagnosis, prognosis, and treatment of ophthalmic diseases.Graefe's archive for clinical and experimental ophthalmology = Albrecht von Graefes Archiv fur klinische und experimentelle Ophthalmologie · 2025Review
- Generation of Fundus Fluorescein Angiography Videos for Health Care Data Sharing.JAMA ophthalmology · 2025Article
- Multi-Modal AI for Multi-Label Retinal Disease Prediction Using OCT and Fundus Images: A Hybrid Approach.Sensors (Basel, Switzerland) · 2025Article
- The burden of age-related macular degeneration and its socioeconomic associates in the Eastern Mediterranean Region from 1990 to 2021.Scientific reports · 2025Article
- A multimodal visual-language foundation model for computational ophthalmology.NPJ digital medicine · 2025Article
- Embodied artificial intelligence in ophthalmology.NPJ digital medicine · 2025Review
- Reinforcement-based leveraging transfer learning for multiclass optical coherence tomography images classification.Scientific reports · 2025Article
- A Future Picture: A Review of Current Generative Adversarial Neural Networks in Vitreoretinal Pathologies and Their Future Potentials.Biomedicines · 2025Review
Corrections and comments
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Authors and funding
8 authors at 3 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
Age-related macular degeneration (AMD) is the leading cause of central vision impairment among the elderly. Effective and accurate AMD screening tools are urgently needed. Indocyanine green angiography (ICGA) is a well-established technique for detecting chorioretinal diseases, but its invasive nature and potential risks impede its routine clinical application. Here, we innovatively developed a deep-learning model capable of generating realistic ICGA images from color fundus photography (CF) using generative adversarial networks (GANs) and evaluated its performance in AMD classification. The model was developed with 99,002 CF-ICGA pairs from a tertiary center. The quality of the generated ICGA images underwent objective evaluation using mean absolute error (MAE), peak signal-to-noise ratio (PSNR), structural similarity measures (SSIM), etc., and subjective evaluation by two experienced ophthalmologists. The model generated realistic early, mid and late-phase ICGA images, with SSIM spanned from 0.57 to 0.65. The subjective quality scores ranged from 1.46 to 2.74 on the five-point scale (1 refers to the real ICGA image quality, Kappa 0.79-0.84). Moreover, we assessed the application of translated ICGA images in AMD screening on an external dataset (n = 13887) by calculating area under the ROC curve (AUC) in classifying AMD. Combining generated ICGA with real CF images improved the accuracy of AMD classification with AUC increased from 0.93 to 0.97 (P < 0.001). These results suggested that CF-to-ICGA translation can serve as a cross-modal data augmentation method to address the data hunger often encountered in deep-learning research, and as a promising add-on for population-based AMD screening. Real-world validation is warranted before clinical usage.
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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.