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In ophthalmology, intravitreal operative medication therapy (IVOM) is awidespread treatment for diseases related to the age-related maculardegeneration (AMD), the diabetic macular edema (DME), as well as the retinalvein occlusion (RVO). However, in real-world settings, patients often sufferfrom loss of vision on time scales of years despite therapy, whereas theprediction of the visual acuity (VA) and the earliest possible detection ofdeterioration under real-life conditions is challenging due to heterogeneousand incomplete data. In this contribution, we present a workflow for thedevelopment of a research-compatible data corpus fusing different IT systems ofthe department of ophthalmology of a German maximum care hospital. Theextensive data corpus allows predictive statements of the expected progressionof a patient and his or her VA in each of the three diseases. We found out forthe disease AMD a significant deterioration of the visual acuity over time.Within our proposed multistage system, we classify the VA progression into thethree groups of therapy "winners", "stabilizers", and "losers" (WSL scheme).Our OCT biomarker classification using an ensemble of deep neural networksresults in a classification accuracy (F1-score) of over 98 %, enabling us tocomplete incomplete OCT documentations while allowing us to exploit them for amore precise VA modelling process. Our VA prediction requires at least four VAexaminations and optionally OCT biomarkers from the same time period to predictthe VA progression within a forecasted time frame, whereas our prediction iscurrently restricted to IVOM / no therapy. While achieving a predictionaccuracy of up to 69 % (macro average F1-score) when considering all threeWSL-based progression groups, this corresponds to an improvement by 11.2 % incomparison to our ophthalmic expertise (57.8 %).

Visual Acuity Prediction on Real-Life Patient Data Using a Machine Learning Based Multistage System