[Screening and management of retinal diseases using digital medicine].

BACKGROUND: Modern retinal imaging creates gigantic amounts of data (big data) of anatomic information. At the same time patient numbers and interventions are increasing exponentially.

OBJECTIVE: Introduction of artificial intelligence (AI) for optimization of personalized therapy and diagnosis.

MATERIAL AND METHODS: Deep learning was introduced for automated segmentation and recognition of risk factors and activity levels in retinal diseases.

RESULTS: Automated algorithms enable the precise identification and quantification of retinal fluid in all compartments. Early detection 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.

CONCLUSION: Methods using AI constitute a breakthrough perspective for the introduction of individualized medicine and optimization of diagnosis and therapy, screening and prognosis.