The German National Cohort: Ophthalmological Assessment, Baseline Profile and Potential for AI-based Eye Research

ObjectiveTo describe the ophthalmic examination protocol within the German National Cohort (NAKO) / NAKO Gesundheitsstudie, to report the baseline profile of participants undergoing ophthalmological assessment, and to illustrate the potential of these data as a population-based open resource for artificial intelligence (AI) research in eye health. DesignBaseline analysis of ophthalmic data within the nationwide, population-based multicenter prospective NAKO study. Participants48,460 adults in the ophthalmological level 2 module of 205,053 adults enrolled in NAKO, aged 19-74 years, with mean age 48.9 {+/-} 12.5 years and 52.7% male. MethodsAll participants underwent standardized assessments of a wide range of biomedical examinations and detailed questionnaire-based data collection, including non-dilated color fundus imaging, visual acuity testing, recording of a brief ocular history. Ocular and systemic health measures were summarized using descriptive statistics. Fundus image quality and morphological features (e.g. cup-to-disc ratio, ateriole-to-venule-ratio) were assessed using open-source deep learning models. Standard deep learning architectures were trained on the fundus images to predict age, sex and blood pressure. Main Outcome MeasuresPercentage of fundus images graded as good quality; mean absolute error for age and blood pressure prediction; accuracy for sex prediction. ResultsThe analysis includes 48,460 participants who successfully completed the level 2 ophthalmological baseline examination across 18 study sites in Germany. Mean visual acuity (logMAR) was 0.01 {+/-} 0.20 (left eye) and 0.03 {+/-} 0.21 (right eye). Self-reported ocular disease prevalence was 4.2% for cataract, 2.0% for glaucoma, and 0.9% for macular degeneration. 68.2% of fundus images were classified as gradable as a consensus of four deep learning-based quality grading models Morphological features such as cup-to-disc ratio and arteriole-to-venule-ratio showed systematic differences across age groups. Standard deep learning architectures showed comparative performance to the state-of-the-art for age, sex and blood pressure prediction (2.96 MAE for age prediction, 0.84 accuracy for sex prediction, 10.78 and 7.01 MAE for systolic and diastolic blood pressure prediction). ConclusionsNAKO provides a large-scale, nationwide population-based resource with visual acuity measurements and systemic health indicators, as well as color fundus images in about 50,000 NAKO participants. The data sets the ground for studying eye health in the general adult population in Germany and can serve as a strong foundation for developing and validating AI tools in eye health research.