The Application Of Ai And Other Advanced Technology In Studying Eye Diseases And Visual Development

The Application of AI and Other Advanced Technology in Studying Eye Diseases and Visual Development

Artificial Intelligence in Ophthalmology

This book provides a wide-ranging overview of artificial intelligence (AI), machine learning (ML) and deep learning (DL) algorithms in ophthalmology. Expertly written chapters examine AI in age-related macular degeneration, glaucoma, retinopathy of prematurity and diabetic retinopathy screening. AI perspectives, systems and limitations are all carefully assessed throughout the book as well as the technical aspects of DL systems for retinal diseases including the application of Google DeepMind, the Singapore algorithm, and the Johns Hopkins algorithm. Artificial Intelligence in Ophthalmology meets the need for a resource that reviews the benefits and pitfalls of AI, ML and DL in ophthalmology. Ophthalmologists, optometrists, eye-care workers, neurologists, cardiologists, internal medicine specialists, AI engineers and IT specialists with an interest in how AI can help with early diagnosis and monitoring treatment in ophthalmic patients will find this book to be an indispensable guide to an evolving area of healthcare technology.

Efficient Artificial Intelligence (AI) in Ophthalmic Imaging

Ophthalmic imaging techniques, such as optical coherence tomography (OCT), OCT angiography (OCTA), fundus photography, and fluorescein angiography, generate vast amounts of visual data, allowing for a detailed examination of the eye's structure and function. These images provide invaluable insights into the presence and progression of various ocular diseases, such as age-related macular degeneration, diabetic retinopathy, and glaucoma. While ophthalmologists possess expertise in interpreting these images, the manual analysis of large datasets can be time-consuming, prone to errors, and subject to inter-observer variability. Artificial Intelligence (AI) based approaches have emerged as promising tools to augment the diagnostic capabilities of ophthalmologists, enabling faster and more accurate assessments. This proposal aims to explore and develop innovative approaches, including but not limited to transfer learning, activate learning, semi-supervised learning, weakly supervised learning, meta-learning, graph neural networks, multimodal learning, and federated learning, etc. to enhance the efficiency and effectiveness of AI in ophthalmic imaging. This collection aims to promote research that improves the accuracy, scalability, interpretability, and generalization of AI models for ophthalmic imaging. This Research Topic welcomes manuscripts on the following themes: ● Annotation-efficient AI: Manual data annotation is a labor-intensive and time consuming process that often poses challenges in terms of scalability, cost, and subjectivity. This topic aims to explore innovative approaches and techniques to develop annotation-efficient AI models that can leverage minimal data annotation while maintaining high performance and generalization, such as activate learning, semi-/weakly/un-/self-supervised learning, etc.