Stolze Gabriel, Kakkassery Vinodh, Kowerko Danny, Bartos Martin, Hoffmann Katja, Sedlmayr Martin, Engelmann Katrin
Klinik für Augenheilkunde, Klinikum Chemnitz gGmbH, Flemmingstraße 2, 09116, Chemnitz, Deutschland.
Fakultät für Informatik, Juniorprofessur Media Computing, Technische Universität Chemnitz, Straße der Nationen 62, 09111, Chemnitz, Deutschland.
Ophthalmologie. 2025 Apr;122(4):262-269. doi: 10.1007/s00347-024-02146-x. Epub 2024 Dec 18.
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.
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.
Step by step, the necessary partners in hospitals and practices will be brought together technically or through research questions within Use Case 1 "Ophthalmology meets Diabetology", a regional digital progress hub in health, the medical informatics hub in Saxony (MiHUBx ) of the nationwide medical informatics initiative (MII).
Based on joint studies with diabetologists, robust serological and imaging biomarkers were selected that provide evidence of the development of diabetic macular edema (DME). In the future, these and other scientifically proven prognostic markers will be incorporated into a treatment algorithm that is supported by artificial intelligence (AI). For this purpose, model procedures are being developed together with medical informatics specialists. At the same time, a data integration center (DIZ) was established.
In addition to the structured and technical combination of the previously disseminated and partially heterogeneous treatment data, the Use Case 1 defines the chances and hurdles for using such real-world data to develop artificial intelligence.
基于证据的治疗建议有助于相应学科的特定治疗，但很难考虑到真实世界护理中的数据。为了在日常临床实践中更好地利用这些数据，包括对疾病发展或治疗成功的预测性陈述，必须开发包含治疗数据的模型，以便将其用于辅助人工智能的开发。
萨克森医学信息中心（MiHUBx）用例1的目标是基于治疗和研究数据开发一个由生物标志物支持的治疗算法模型，并开发必要的数字基础设施。
在“眼科与糖尿病学相遇”这一用例1中，将逐步通过技术手段或研究问题把医院和诊所中必要的合作伙伴聚集在一起。“眼科与糖尿病学相遇”是一个地区性的健康数字进步中心，也是全国医学信息倡议（MII）中的萨克森医学信息中心（MiHUBx）。
通过与糖尿病专家的联合研究，选择了强大的血清学和影像学生物标志物，这些标志物为糖尿病性黄斑水肿（DME）的发展提供了证据。未来，这些以及其他经过科学验证的预后标志物将被纳入一个由人工智能（AI）支持的治疗算法中。为此，正在与医学信息专家共同开发模型程序。同时，还建立了一个数据整合中心（DIZ）。
除了对先前分散且部分异构的治疗数据进行结构化和技术整合外，用例1还定义了利用此类真实世界数据开发人工智能的机会和障碍。
