[MiHUBx: a digital progress hub for the use of intersectoral clinical data sets using the example of diabetic macular edema].

Background: Evidence-based treatment recommendations are helpful in the corresponding discipline-specific treatment but can hardly take data from real-world care into account. In order to make better use of this in everyday clinical practice, including with respect to predictive statements about disease development or treatment success, models with data from treatment must be developed in order to use them for the development of assistive artificial intelligence.

Goal: The aim of the Use Case 1 of the medical informatics hub in Saxony (MiHUBx) is the development of a model based on treatment and research data for a treatment algorithm supported by biomarkers and also the development of the necessary digital infrastructure.

Towards Standardizing Ophthalmic Data for Seamless Interoperability in Eye Care.

Introduction: Seamless interoperability of ophthalmic clinical data is beneficial for improving patient care and advancing research through the integration of data from various sources. Such consolidation increases the amount of data available, leading to more robust statistical analyses, and improving the accuracy and reliability of artificial intelligence models. However, the lack of consistent, harmonized data formats and meanings (syntactic and semantic interoperability) poses a significant challenge in sharing ophthalmic data.

Visual acuity prediction on real-life patient data using a machine learning based multistage system.

In ophthalmology, intravitreal operative medication therapy (IVOM) is a widespread treatment for diseases related to the age-related macular degeneration (AMD), the diabetic macular edema, as well as the retinal vein occlusion. However, in real-world settings, patients often suffer from loss of vision on time scales of years despite therapy, whereas the prediction of the visual acuity (VA) and the earliest possible detection of deterioration under real-life conditions is challenging due to heterogeneous and incomplete data. In this contribution, we present a workflow for the development of a research-compatible data corpus fusing different IT systems of the department of ophthalmology of a German maximum care hospital.