This is the Volume II of the Research Topic "Artificial Intelligence Applications in Chronic Ocular Diseases", https://www.frontiersin.org/research-topics/46069/artificial-intelligence-applications-in-chronic-ocular-diseases/magazine.
With the continuous acceleration of urbanization and aging processes and changes in lifestyles, chronic ocular diseases have become a critical threat to the vision health of the global population. These diseases are spreading globally and have evolved into crucial public health problems that seriously endanger human health and sustainable social and economic development. Chronic ocular diseases include a series of disorders and conditions that involve long-term defects in both anterior and posterior segments of the eye, such as cataract, glaucoma, keratoconus (KC), diabetic retinopathy (DR), and age-related macular degeneration (AMD). Research on artificial intelligence (AI) and its related applications will provide new strategies for preventing, diagnosing, and treating these chronic eye diseases. Therefore, it becomes highly valued by governments and societies worldwide.
Various artificial intelligence applications have been developed to facilitate the research of chronic ocular diseases, including applications in cell research like big data analyses in the field of molecular biology, and ocular chronic disease screening and diagnostic applications using ophthalmic imaging systems. In addition, AI algorithms like Convolutional Neural Networks (CNN) and long short-term memory (LSTM) have been used in chronic ocular disease progression tracking, disease outcome prediction, and treatment endpoint estimation. These methods provide great versatility in addressing different age-related and pathophysiological mechanisms, such as structural disorders (such as corneal thinning and retinocortical atrophy), ischemia-hypoxia conditions (such as choroidal perfusion decline), and functional abnormalities (such as neurovascular decoupling).
This Research Topic aims to provide an improved understanding of the artificial intelligence applications in chronic ocular diseases, in the hope that this will be helpful for a better illustration of how these diseases develop and affect individuals, facilitating early diagnosis and treatment, as well as clarifying the pathological mechanisms.
Potential topics include but are not limited to the following:
• AI or big data analyses in the field of molecular biology;
• New findings of AI-related to ocular chronic diseases progression, classification, and prediction;
• New AI-based ophthalmic image analysis methods to predict the ocular chronic diseases development and treatment outcome;
• New technology related to AI in detecting biomarkers of chronic ocular diseases;
• AI-related biomarkers collection, preparation, and detection;
• Treatments based on AI-based biomarkers analysis in chronic ocular disease.
Keywords:
artificial intelligence applications, ocular diseases, chronic diseases, big data analyses, disease screening and diagnosis
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.
This is the Volume II of the Research Topic "Artificial Intelligence Applications in Chronic Ocular Diseases", https://www.frontiersin.org/research-topics/46069/artificial-intelligence-applications-in-chronic-ocular-diseases/magazine.
With the continuous acceleration of urbanization and aging processes and changes in lifestyles, chronic ocular diseases have become a critical threat to the vision health of the global population. These diseases are spreading globally and have evolved into crucial public health problems that seriously endanger human health and sustainable social and economic development. Chronic ocular diseases include a series of disorders and conditions that involve long-term defects in both anterior and posterior segments of the eye, such as cataract, glaucoma, keratoconus (KC), diabetic retinopathy (DR), and age-related macular degeneration (AMD). Research on artificial intelligence (AI) and its related applications will provide new strategies for preventing, diagnosing, and treating these chronic eye diseases. Therefore, it becomes highly valued by governments and societies worldwide.
Various artificial intelligence applications have been developed to facilitate the research of chronic ocular diseases, including applications in cell research like big data analyses in the field of molecular biology, and ocular chronic disease screening and diagnostic applications using ophthalmic imaging systems. In addition, AI algorithms like Convolutional Neural Networks (CNN) and long short-term memory (LSTM) have been used in chronic ocular disease progression tracking, disease outcome prediction, and treatment endpoint estimation. These methods provide great versatility in addressing different age-related and pathophysiological mechanisms, such as structural disorders (such as corneal thinning and retinocortical atrophy), ischemia-hypoxia conditions (such as choroidal perfusion decline), and functional abnormalities (such as neurovascular decoupling).
This Research Topic aims to provide an improved understanding of the artificial intelligence applications in chronic ocular diseases, in the hope that this will be helpful for a better illustration of how these diseases develop and affect individuals, facilitating early diagnosis and treatment, as well as clarifying the pathological mechanisms.
Potential topics include but are not limited to the following:
• AI or big data analyses in the field of molecular biology;
• New findings of AI-related to ocular chronic diseases progression, classification, and prediction;
• New AI-based ophthalmic image analysis methods to predict the ocular chronic diseases development and treatment outcome;
• New technology related to AI in detecting biomarkers of chronic ocular diseases;
• AI-related biomarkers collection, preparation, and detection;
• Treatments based on AI-based biomarkers analysis in chronic ocular disease.
Keywords:
artificial intelligence applications, ocular diseases, chronic diseases, big data analyses, disease screening and diagnosis
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.