About this Research Topic
Following the success of Efficient Artificial Intelligence (AI) in Ophthalmic Imaging, we are pleased to present Volume II of this collection.
In the field of ophthalmology, imaging technologies like optical coherence tomography (OCT), OCT angiography (OCTA), fundus photography, and fluorescein angiography have become pivotal. These modalities produce considerable visual data, crucial for identifying and monitoring ocular diseases such as age-related macular degeneration, diabetic retinopathy, and glaucoma. Although ophthalmologists are highly skilled at interpreting these images, manual analysis proves both time-consuming and susceptible to inconsistency and error. Emerging AI techniques promise substantial improvements by enhancing speed and accuracy in image interpretation, which could significantly impact diagnostics.
This Research Topic aims to discuss AI methodologies which optimize the efficiency and effectiveness of ophthalmic imaging analyses. By exploring advanced concepts such as transfer learning, active learning, semi-supervised learning, and other innovative AI paradigms, we seek to promote research which pushes the boundaries of what's possible in AI-assisted diagnostics, enhancing the reliability and generalization of AI models used in this meaningful context.
To gather further insights in achieving high-performance yet resource-efficient AI applications, we welcome articles addressing, but not limited to, the following themes as applied to ophthalmic imaging:
• Annotation-efficient AI: Developing AI models that require minimal manual data annotation while maintaining high performance and generalization.
• Training-efficient AI: Innovating techniques to reduce computational demands and streamline the training processes, enabling wider implementation.
• Inference-efficient AI: Optimizing algorithms and model designs for faster deployment and efficient runtime performance across multiple devices.
• Communication-efficient AI: Improving data exchange efficiency in distributed or federated learning settings to facilitate collaborative and privacy-preserving learning.
• Domain-efficient AI: Enabling AI to quickly adapt to new domains or tasks, particularly where data labeling is challenging or impractical.
• LLM-enhanced vision language models: Exploring models that integrate computer vision and natural language processing techniques to improve interpretation and diagnostic accuracy.
In the field of ophthalmology, imaging technologies like optical coherence tomography (OCT), OCT angiography (OCTA), fundus photography, and fluorescein angiography have become pivotal. These modalities produce considerable visual data, crucial for identifying and monitoring ocular diseases such as age-related macular degeneration, diabetic retinopathy, and glaucoma. Although ophthalmologists are highly skilled at interpreting these images, manual analysis proves both time-consuming and susceptible to inconsistency and error. Emerging AI techniques promise substantial improvements by enhancing speed and accuracy in image interpretation, which could significantly impact diagnostics.
This Research Topic aims to discuss AI methodologies which optimize the efficiency and effectiveness of ophthalmic imaging analyses. By exploring advanced concepts such as transfer learning, active learning, semi-supervised learning, and other innovative AI paradigms, we seek to promote research which pushes the boundaries of what's possible in AI-assisted diagnostics, enhancing the reliability and generalization of AI models used in this meaningful context.
To gather further insights in achieving high-performance yet resource-efficient AI applications, we welcome articles addressing, but not limited to, the following themes as applied to ophthalmic imaging:
• Annotation-efficient AI: Developing AI models that require minimal manual data annotation while maintaining high performance and generalization.
• Training-efficient AI: Innovating techniques to reduce computational demands and streamline the training processes, enabling wider implementation.
• Inference-efficient AI: Optimizing algorithms and model designs for faster deployment and efficient runtime performance across multiple devices.
• Communication-efficient AI: Improving data exchange efficiency in distributed or federated learning settings to facilitate collaborative and privacy-preserving learning.
• Domain-efficient AI: Enabling AI to quickly adapt to new domains or tasks, particularly where data labeling is challenging or impractical.
• LLM-enhanced vision language models: Exploring models that integrate computer vision and natural language processing techniques to improve interpretation and diagnostic accuracy.
Keywords: ophthalmic imaging, optical coherence tomography, OCT angiography, OCTA, fundus photography, artificial intelligence, fluorescein angiography, anterior segment photography, corneal topographic imaging, confocal scanning laser ophthalmoscopy
Important Note: All contributions to this Research Topic must be within the scope of the section and journal to which they are submitted, as defined in their mission statements. Frontiers reserves the right to guide an out-of-scope manuscript to a more suitable section or journal at any stage of peer review.
