About this Research Topic
Retinal disease is one of the most important challenges in the field of ophthalmology, with complex pathogenesis. It can severely impair visual function and is a major cause of vision loss in humans. Retinal diseases include Age-related Macular Degeneration (AMD), Diabetic Retinopathy (DR), etc. In recent years, artificial intelligence (AI) technology has provided a powerful tool for the intelligent analysis of retinal diseases. It can automatically extract disease-related features from retinal images to assist clinicians in improving diagnostic efficiency. In addition, it can explore potential disease-related biomarkers and promote the development of retinal disease subjects.
Currently, there are many topics worthy of research in retinal disease analysis based on AI technologies. For example, the study of generalized models in small samples, joint analysis of multi-center/multi-modality (image, text) samples, interpretability of AI models and disease features, mining of potential biomarkers and intrinsic connections between retinal diseases and systemic diseases, disease prognosis, etcetera. In-depth research on the above topics can achieve an accurate and rapid diagnosis of retinal diseases, and promote the large-scale deployment of AI models in clinical practice, which is of great significance.
This Research Topic aims to promote the deep fusion of AI technology and retinal disease analysis and overcome the challenges of AI methods in small datasets, multi-center/multi-modality, rare disease samples shortage, and so on. It will also stimulate scholars' research interest in novel interpretable methods, potential biomarker exploration, disease prognosis, and more. In addition, this Research Topic will actively contribute to the clinical applications of AI technology to form a mature system for disease screening, diagnosis grading, treatment, and prognosis of retinal diseases.
● Generalizable methods for small training datasets
- Label imbalance problem
- Few-shot learning
- Generative data augmentation
- Integration of prior expert knowledge
● Generalizable methods for domain shift
- Cross-device/ Cross-modality generalization
- Zero-shot learning
- Transfer learning
- Test-time Adaption
● Anomaly detection for retina disease
- Out-of-distribution analysis
- Label efficient learning
- Ensemble model
- Generative method
● Explainable/interpretable methods
- Novel interpretable method for disease characterization
- Learning interpretable knowledge from large-scale pre-trained models
- Uncertainty estimation
- Incorporation of text knowledge into retina disease analysis
● Relevant applications of generalizable and explainable methodologies
- Disease screening
- Biomarker discovery
- Relevance exploration of physiological indexes and diseases
- Quantitative analysis of lesion
- Prediction of disease trend
Currently, there are many topics worthy of research in retinal disease analysis based on AI technologies. For example, the study of generalized models in small samples, joint analysis of multi-center/multi-modality (image, text) samples, interpretability of AI models and disease features, mining of potential biomarkers and intrinsic connections between retinal diseases and systemic diseases, disease prognosis, etcetera. In-depth research on the above topics can achieve an accurate and rapid diagnosis of retinal diseases, and promote the large-scale deployment of AI models in clinical practice, which is of great significance.
This Research Topic aims to promote the deep fusion of AI technology and retinal disease analysis and overcome the challenges of AI methods in small datasets, multi-center/multi-modality, rare disease samples shortage, and so on. It will also stimulate scholars' research interest in novel interpretable methods, potential biomarker exploration, disease prognosis, and more. In addition, this Research Topic will actively contribute to the clinical applications of AI technology to form a mature system for disease screening, diagnosis grading, treatment, and prognosis of retinal diseases.
● Generalizable methods for small training datasets
- Label imbalance problem
- Few-shot learning
- Generative data augmentation
- Integration of prior expert knowledge
● Generalizable methods for domain shift
- Cross-device/ Cross-modality generalization
- Zero-shot learning
- Transfer learning
- Test-time Adaption
● Anomaly detection for retina disease
- Out-of-distribution analysis
- Label efficient learning
- Ensemble model
- Generative method
● Explainable/interpretable methods
- Novel interpretable method for disease characterization
- Learning interpretable knowledge from large-scale pre-trained models
- Uncertainty estimation
- Incorporation of text knowledge into retina disease analysis
● Relevant applications of generalizable and explainable methodologies
- Disease screening
- Biomarker discovery
- Relevance exploration of physiological indexes and diseases
- Quantitative analysis of lesion
- Prediction of disease trend
Keywords: retinal disease analysis, artificial intelligence, generalizable method, explainable method, clinical application
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