About this Research Topic
This Research Topic is the second volume in the series: Artificial Intelligence Applications in Chronic Ocular Diseases.
The previous volume can be viewed here: Volume I
Background：
The field of artificial intelligence (AI) applications in chronic ocular diseases is rapidly evolving, driven by the increasing prevalence of these conditions due to urbanization, aging populations, and lifestyle changes. Chronic ocular diseases, such as cataract, glaucoma, keratoconus, diabetic retinopathy, and age-related macular degeneration, pose significant threats to global vision health and have become pressing public health concerns. These diseases not only impact individual health but also have broader implications for social and economic development. Recent advancements in AI have opened new avenues for the prevention, diagnosis, and treatment of these conditions. AI applications, including big data analyses in molecular biology and ophthalmic imaging systems, have shown promise in screening and diagnosing chronic ocular diseases. Furthermore, AI algorithms like Convolutional Neural Networks (CNN) and long short-term memory (LSTM) are being utilized to track disease progression, predict outcomes, and estimate treatment endpoints. Despite these advancements, there remains a need for further research to fully harness AI's potential in understanding and managing chronic ocular diseases.
Goal：
This research topic aims to provide an improved understanding of artificial intelligence applications in chronic ocular diseases, with the goal of enhancing early diagnosis and treatment, elucidating disease development, and clarifying pathological mechanisms. By exploring AI's role in these areas, the research seeks to answer critical questions about how these diseases progress and affect individuals. The objective is to leverage AI to facilitate better health outcomes and improve the quality of life for those affected by chronic ocular diseases.
Scope：
To gather further insights in the application of AI in chronic ocular diseases, we welcome articles addressing, but not limited to, the following themes:
- 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 ocular chronic diseases development and treatment outcomes.
- 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.
The previous volume can be viewed here: Volume I
Background：
The field of artificial intelligence (AI) applications in chronic ocular diseases is rapidly evolving, driven by the increasing prevalence of these conditions due to urbanization, aging populations, and lifestyle changes. Chronic ocular diseases, such as cataract, glaucoma, keratoconus, diabetic retinopathy, and age-related macular degeneration, pose significant threats to global vision health and have become pressing public health concerns. These diseases not only impact individual health but also have broader implications for social and economic development. Recent advancements in AI have opened new avenues for the prevention, diagnosis, and treatment of these conditions. AI applications, including big data analyses in molecular biology and ophthalmic imaging systems, have shown promise in screening and diagnosing chronic ocular diseases. Furthermore, AI algorithms like Convolutional Neural Networks (CNN) and long short-term memory (LSTM) are being utilized to track disease progression, predict outcomes, and estimate treatment endpoints. Despite these advancements, there remains a need for further research to fully harness AI's potential in understanding and managing chronic ocular diseases.
Goal：
This research topic aims to provide an improved understanding of artificial intelligence applications in chronic ocular diseases, with the goal of enhancing early diagnosis and treatment, elucidating disease development, and clarifying pathological mechanisms. By exploring AI's role in these areas, the research seeks to answer critical questions about how these diseases progress and affect individuals. The objective is to leverage AI to facilitate better health outcomes and improve the quality of life for those affected by chronic ocular diseases.
Scope：
To gather further insights in the application of AI in chronic ocular diseases, we welcome articles addressing, but not limited to, the following themes:
- 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 ocular chronic diseases development and treatment outcomes.
- 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.
