Screening and management of retinal diseases using digital medicine

BackgroundModern retinal imaging creates gigantic amounts of data (big data) of anatomic information. At the same time patient numbers and interventions are increasing exponentially.ObjectiveIntroduction of artificial intelligence (AI) for optimization of personalized therapy and diagnosis.Material and methodsDeep learning was introduced for automated segmentation and recognition of risk factors and activity levels in retinal diseases.ResultsAutomated algorithms enable the precise identification and quantification of retinal fluid in all compartments.Earlydetection of retinopathy in diabetes or glaucoma or risk determination for the development of age-related macular degeneration (AMD) are possible as well as an individual visual prognosis and evaluation of the need for retreatment in intravitreal injection therapy.ConclusionMethods using AIconstitute abreakthrough perspective for the introduction of individualized medicine and optimization of diagnosis and therapy, screening and prognosis.