The German National Cohort: Ophthalmic Assessment, Baseline Profile, and Potential for Artificial Intelligence-Based Eye Research.
23 affiliations
- 1Hertie Institute for AI in Brain Health, University of Tübingen, Tübingen, Germany.
- 2Tübingen AI Center, University of Tübingen, Tübingen, Germany.
- 3Department of Ophthalmology, University Medical Center Hamburg-Eppendorf (UKE), Hamburg, Germany.
- 4University Eye Hospital, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany.
- 5Institute of Epidemiology and Social Medicine, University of Münster, Münster, Germany.
- 6Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany.
- 7Department of Ophthalmology, University of Regensburg, Regensburg, Germany.
- 8German Cancer Research Center (DKFZ) Heidelberg, Division of Cancer Epidemiology, Heidelberg, Germany.
- 9Department of Epidemiological Methods and Etiological Research, Leibniz Institute for Prevention Research and Epidemiology - BIPS, Bremen, Germany.
- 10Department of Ophthalmology, Medical Faculty Mannheim, University of Heidelberg, Mannheim, Germany.
- 11Department of Ophthalmology, Heinrich-Heine University, Düsseldorf, Germany.
- 12Institute of Social Medicine, Epidemiology and Health Economics, Charité - Universitätsmedizin Berlin, Berlin, Germany.
- 13Institute of Clinical Epidemiology and Biometry, University of Würzburg, Würzburg, Germany.
- 14State Institute of Health I, Bavarian Health and Food Safety Authority, Erlangen, Germany.
- 15Helmholtz Center for Infection Research, Braunschweig, Germany.
- 16Institute for Epidemiology and Prevention Medicine, University of Regensburg, Regensburg, Germany.
- 17Institut für Community Medicine, Universitätsmedizin Greifswald, Greifswald, Germany.
- 18Chair of Epidemiology, IBE, Faculty of Medicine, LMU Munich, Germany.
- 19Helmholtz Zentrum München, German Research Center for Environmental Health (GmbH), Institute of Epidemiology, Neuherberg, Germany.
- 20Department of Molecular Epidemiology, German Institute of Human Nutrition Potsdam-Rehbruecke, Nuthetal, Germany.
- 21Institute of Nutritional Science, University of Potsdam, Nuthetal, Germany.
- 22Department of Ophthalmology, University Medical Center of the Johannes Gutenberg-University Mainz, Mainz, Germany.
- 23Institute for Ophthalmic Research, Centre for Ophthalmology, University of Tübingen, Tübingen, Germany.
Abstract
Objective
To describe the ophthalmic examination protocol within the German National Cohort (NAKO)/NAKO Gesundheitsstudie, to report the baseline profile of participants undergoing ophthalmic assessment, and to illustrate the potential as a population-based resource for artificial intelligence (AI) research in eye health.Design
Baseline analysis of ophthalmic data within the nationwide, population-based multicenter prospective NAKO study.Participants
Forty-eight thousand four hundred and sixty adults in the ophthalmic level 2 module of 205 053 adults enrolled in NAKO, aged 19-74 years, with a mean age 48.9 ± 12.5 years, and 52.7% were male.Methods
All participants underwent standardized biomedical examinations and detailed questionnaire-based data collection, including nondilated color fundus imaging, visual acuity testing, and a brief ocular history. Ocular health was summarized using descriptive statistics. Fundus image quality and morphological features (e.g., cup-to-disc ratio and arteriolar-to-venular 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 measures
Percentage of fundus images graded as good quality; mean absolute error (MAE) for age and blood pressure prediction; accuracy for sex prediction.Results
The analysis includes 48 460 participants who completed the ophthalmic baseline examination of NAKO. Mean visual acuity (logarithm of the minimum angle of resolution) was 0.01 ± 0.20 (left eye, mean ± standard deviation) 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 using 4 deep learning-based quality grading models. Morphological features 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, and 10.78 and 6.97 MAE for systolic and diastolic blood pressure prediction).Conclusions
The German National Cohort 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. These data lay the groundwork 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.Financial disclosures
Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
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BibTeX
@article{roa2026german,
title = {{The German National Cohort: Ophthalmic Assessment, Baseline Profile, and Potential for Artificial Intelligence-Based Eye Research.}},
author = {Camila Roa and Ansgar Beuse and Alexandra Schweig and Sarah Müller and Klaus Berger and Caroline Brandl and Titus Brinker and Anne Elbrecht and Robert Finger and Gerd Geerling and Karin Halina Greiser and Carsten Grohmann and Kathrin Günther and Iris Heid and André Karch and Thomas Keil and Jessica Krepel and Michael Leitzmann and Claudia Meinke-Franze and Annette Peters and Sabine Schipf and Matthias Schulze and Alexander K Schuster and Stefan N Willich and Martin A Leitritz and Marius Ueffing and Philipp Berens},
journal = {Ophthalmology science},
year = {2026},
month = sep,
doi = {10.1016/j.xops.2026.101408},
url = {https://europepmc.org/article/MED/42894708},
note = {CC BY},
}
RIS
TY - JOUR
TI - The German National Cohort: Ophthalmic Assessment, Baseline Profile, and Potential for Artificial Intelligence-Based Eye Research.
AU - Camila Roa
AU - Ansgar Beuse
AU - Alexandra Schweig
AU - Sarah Müller
AU - Klaus Berger
AU - Caroline Brandl
AU - Titus Brinker
AU - Anne Elbrecht
AU - Robert Finger
AU - Gerd Geerling
AU - Karin Halina Greiser
AU - Carsten Grohmann
AU - Kathrin Günther
AU - Iris Heid
AU - André Karch
AU - Thomas Keil
AU - Jessica Krepel
AU - Michael Leitzmann
AU - Claudia Meinke-Franze
AU - Annette Peters
AU - Sabine Schipf
AU - Matthias Schulze
AU - Alexander K Schuster
AU - Stefan N Willich
AU - Martin A Leitritz
AU - Marius Ueffing
AU - Philipp Berens
PY - 2026
DA - 2026/09/11
JO - Ophthalmology science
DO - 10.1016/j.xops.2026.101408
UR - https://europepmc.org/article/MED/42894708
AB - Objective To describe the ophthalmic examination protocol within the German National Cohort (NAKO)/NAKO Gesundheitsstudie, to report the baseline profile of participants undergoing ophthalmic assessment, and to illustrate the potential as a population-based resource for artificial intelligence (AI) research in eye health.Design Baseline analysis of ophthalmic data within the nationwide, population-based multicenter prospective NAKO study.Participants Forty-eight thousand four hundred and sixty adults in the ophthalmic level 2 module of 205 053 adults enrolled in NAKO, aged 19-74 years, with a mean age 48.9 ± 12.5 years, and 52.7% were male.Methods All participants underwent standardized biomedical examinations and detailed questionnaire-based data collection, including nondilated color fundus imaging, visual acuity testing, and a brief ocular history. Ocular health was summarized using descriptive statistics. Fundus image quality and morphological features (e.g., cup-to-disc ratio and arteriolar-to-venular 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 measures Percentage of fundus images graded as good quality; mean absolute error (MAE) for age and blood pressure prediction; accuracy for sex prediction.Results The analysis includes 48 460 participants who completed the ophthalmic baseline examination of NAKO. Mean visual acuity (logarithm of the minimum angle of resolution) was 0.01 ± 0.20 (left eye, mean ± standard deviation) 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 using 4 deep learning-based quality grading models. Morphological features 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, and 10.78 and 6.97 MAE for systolic and diastolic blood pressure prediction).Conclusions The German National Cohort 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. These data lay the groundwork 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.Financial disclosures Proprietary or commercial disclosure may be found in the Footnotes and Disclosures at the end of this article.
SN - 2666-9145
ER -