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 (DME), as well as the retinal vein occlusion (RVO). 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. The extensive data corpus allows predictive statements of the expected progression of a patient and his or her VA in each of the three diseases. We found out for the disease AMD a significant deterioration of the visual acuity over time. Within our proposed multistage system, we classify the VA progression into the three groups of therapy "winners", "stabilizers", and "losers" (WSL scheme). Our OCT biomarker classification using an ensemble of deep neural networks results in a classification accuracy (F1-score) of over 98 %, enabling us to complete incomplete OCT documentations while allowing us to exploit them for a more precise VA modelling process. Our VA prediction requires at least four VA examinations and optionally OCT biomarkers from the same time period to predict the VA progression within a forecasted time frame, whereas our prediction is currently restricted to IVOM / no therapy. While achieving a prediction accuracy of up to 69 % (macro average F1-score) when considering all three WSL-based progression groups, this corresponds to an improvement by 11.2 % in comparison to our ophthalmic expertise (57.8 %).

翻译：在眼科领域，玻璃体腔内药物注射治疗是年龄相关性黄斑变性、糖尿病性黄斑水肿及视网膜静脉阻塞相关疾病的常用疗法。然而在真实临床场景中，尽管接受治疗，患者仍常在数年时间尺度上出现视力丧失，而由于数据异质性和不完整性，在真实条件下预测视力并尽早发现病情恶化极具挑战性。本文提出了一套工作流程，用于构建融合德国某最大规模眼科诊疗中心多个信息系统数据的研究兼容性数据集。该大规模数据集使我们能够对上述三种疾病患者的预期病程及视力变化进行预测性判断。研究发现AMD患者的视力随时间显著恶化。在所提出的多阶段系统中，我们将视力进展划分为"治疗获益者"、"病情稳定者"和"治疗无效者"三个组别（WSL方案）。采用深度神经网络集成方法进行OCT生物标志物分类，其分类准确率（F1分数）超过98%，这不仅能够补全不完整的OCT记录，还能利用这些数据进行更精确的视力建模。我们的视力预测需要至少四次视力检查记录，并可选配同期OCT生物标志物，以预测预设时间范围内的视力变化趋势，目前预测范围限于接受/未接受IVOM治疗的情况。当同时考虑三种WSL进展组别时，预测准确率可达69%（宏观平均F1分数），相较眼科专家经验判断的57.8%提高了11.2%。