Molecular and Imaging Biomarkers and Artificial Intelligence in Diabetic Retinopathy
1Guangdong Provincial People's Hospital, Guangzhou, China
2The First Affiliated Hospital of China Medical University, Shenyang, China
3Aier School of Ophthalmology, Changsha, China
4Sun Yat-sen University, Guangzhou, China
5Central South University, Changsha, China
6University of Melbourne, Melbourne, Australia
Molecular and Imaging Biomarkers and Artificial Intelligence in Diabetic Retinopathy
Description
Diabetes mellitus (DM) is a major systemic disorder, imposing a huge global social-economical burden. Ocular complications such as diabetic retinopathy (DR) are common in DM patients, which can lead to severe visual impairment. Early diagnosis and proper management of DR is important for preventing visual impairment in DM patients. There is an urgent need for indicators to predict the occurrence, progression, and treatment outcomes of DR. DR biomarkers – both imaging and molecular – may reshape our understanding of the pathogenesis of the disease and are also particularly useful for early detection and prognosis prediction of DR. Although some imaging and molecular biomarkers of DR have been proposed, the journey of finding new and better biomarkers will continue.
In recent years, artificial intelligence (AI) has been implemented into the management of ocular diseases. AI techniques are capable of accurate diagnosis and outcome prediction and can be used to explore imaging and molecular biomarkers of DR, in addition to helping refine DR management strategies.
In this Special Issue, original research and review articles about imaging and molecular biomarkers and AI in DR are welcome. We aim to summarize the latest advancements in clinical studies or reviews on imaging and molecular biomarkers related to the diagnosis, progression, and treatment outcomes of DR, and the application of AI techniques in clinical research in DR.
Potential topics include but are not limited to the following:
- Systemic and ocular molecular biomarkers of DR in biological samples, such as blood, intraocular fluid, or tears
- Multimodal retinal imaging biomarkers of DR
- Optical coherence tomography/optical coherence tomography angiography in the diagnosis and management of DR
- Correlation and interaction between imaging and molecular biomarkers of DR
- Application of AI techniques, such as machine learning and deep learning, in automated screening/diagnosis/prognosis prediction of DR
- Exploration of DR biomarkers using AI techniques and big data analysis