Computational Medicine in Visual Impairment and Its Related Disorders

Vision is a vital perception that accounts for more than 80% of the sensory inputs. Individuals with impaired vision will suffer from imprecise spatial orientation and hindered physical activities (e.g., walking). Moreover, multimodal disorders are closely correlated with visual impairment, such as changes in behavioral patterns, neuronal activity, cortical function. The leading causes of visual impairment are cataracts, followed by refractive error, age-related macular degeneration, glaucoma, and diabetic retinopathy. However, the healthcare efficiency for these visually impaired conditions remains unsatisfactory, which has generated a huge social-economic impact.

With the developments of algorithmic analytics and the abundance of genome-phenome information, computational medicine has the potential to revolutionize the screening, diagnosis, and management of visual impairment and its related disorders, through both the automated processing of large data sets and the early risk detection of the risk. In addition, computational medicine holds a promise to fundamentally change research with the goal to understand the development, progression, and treatment of visual impairment, by identifying novel risk factors and by simulating the progressive pattern.

This Research Topic aims to provide an overview of computational strategies and solutions in visual impairment. We would like to highlight how digital health care can improve the clinical outcomes of visual impairment and its related disorders (e.g., prevention, management, and prognosis), and how informatics analyses can contribute to a better understanding of genetic predisposition in visual impairment.

We welcome investigators to contribute their Original Research, Methods, Review, Mini Review, Hypothesis and Theory, Perspective, Clinical Trial, Case Report, Data Report, Brief Research Report, on different aspects of computational contributions on the management and understandings of visual impairment. Some potential topics, which are non-exclusive, include:
 Application of cutting-edge technologies (e.g., machine learning, polygenic model, fMRI) in screening, diagnosis, management, and risk prediction of visual impairment and its related disorders.
 Computational modeling of the clinical dynamics of visual impairment and its related disorders (e.g., progression and prognosis)
 Further insight into visual impairment driven by multi-omics analysis from genomics to phenomics
 Environmental and developmental effects (e.g., deprivation) on visual impairment and its related disorders by longitudinal analyses