With the acceleration of urbanization and aging processes, chronic ocular diseases have become a critical threat to the vision health of the global population . 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). However, it is still challenging to understand the mechanisms of these diseases and to discover new and reliable biomarkers to identify the diseases and their severities. Moreover, conventional methods in clinical societies are not as effective or efficient as expected, especially in the era of artificial intelligence.
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 for chronic ocular disease research;
• 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 for chronic ocular disease research;
• Treatments based on AI-based biomarkers analysis in chronic ocular disease.
Topic Editor Dr. Yanwu Xu is employed by Baidu, China and Topcon Healthcare, China. All other Topic Editors declare no competing interests with regards to the Research Topic subject.
With the acceleration of urbanization and aging processes, chronic ocular diseases have become a critical threat to the vision health of the global population . 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). However, it is still challenging to understand the mechanisms of these diseases and to discover new and reliable biomarkers to identify the diseases and their severities. Moreover, conventional methods in clinical societies are not as effective or efficient as expected, especially in the era of artificial intelligence.
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 for chronic ocular disease research;
• 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 for chronic ocular disease research;
• Treatments based on AI-based biomarkers analysis in chronic ocular disease.
Topic Editor Dr. Yanwu Xu is employed by Baidu, China and Topcon Healthcare, China. All other Topic Editors declare no competing interests with regards to the Research Topic subject.