Heidelberg, Germany — Heidelberg Engineering proudly announces a significant advance in the use of artificial intelligence for ophthalmic diagnostics, with the publication of the Eye2Gene™ study in Nature Machine Intelligence. Titled “Next-generation phenotyping of inherited retinal diseases from multimodal imaging with Eye2Gene”, the study highlights the potential of AI-powered analysis of multimodal SPECTRALIS® imaging to accelerate genetic diagnosis in patients with inherited retinal diseases (IRDs).
Eye2Gene leverages Fundus Autofluorescence (FAF), Infrared Reflectance (IR), and spectral-domain OCT (SD-OCT) to predict the likely causative gene in IRD cases—providing a non-invasive decision-support tool for clinicians.
A Deep Learning Model with Expert-Level Performance
The AI system was trained on 58,030 multimodal retinal scans from 2,451 patients with confirmed genetic diagnoses and further externally validated on 775 patients from five sites. Covering 63 disease-associated genes, Eye2Gene captures more than 90% of IRD cases in Europe, demonstrating a broad clinical relevance.
“I am very excited to announce the publication of our longanticipated Eye2Gene paper,” said Associate Professor Nikolas Pontikos (UCL), lead author of the study. “We demonstrate a top5 prediction accuracy of 83% compared to world-leading experts.”
Particularly noteworthy is its superiority in interpreting only FAF images, where it reached an accuracy of 76%, compared to 36% or less by experienced clinicians who took part in the study. These results were consistently reproduced across five independent clinical centers—including institutions in Tokyo, Bonn, São Paulo, Oxford, and Liverpool—demonstrating the model’s robustness and generalizability across populations and imaging standards.
In more than 75% of tested cases, it outperformed popular phenotyping-only tools in prioritizing disease-causing genetic variants, thereby increasing the likelihood of achieving a definitive diagnosis.
Clinical Value and Global Impact
At its core, Eye2Gene is powered by an ensemble of 15 convolutional neural networks—five per imaging modality—which together generate patient-level predictions by averaging across scans and modalities. This architecture not only improves accuracy but also ensures that the system can adapt to variations in imaging conditions across different sites.
The clinical implications of Eye2Gene are wide-ranging. The tool supports earlier referrals to genetic testing and clinical trials, assists in complex differential diagnoses, and makes expert-level interpretation accessible in settings where specialist expertise may be limited.
By integrating phenotype data into variant prioritization, Eye2Gene increases the diagnostic yield, improving the likelihood of reaching a genetic diagnosis.
“By combining the power of Heidelberg image quality and AI,” said Arianna Schoess Vargas, Managing Director of Heidelberg Engineering, “we empower eye care professionals with new insights into the genetic landscape of IRDs— enhancing diagnosis and ultimately the development of new treatments.”
Seamless Integration via HEYEX 2 and Heidelberg AppWay
Heidelberg Engineering demonstrated Eye2Gene live at ARVO 2024 and 2025, where participants experienced its integration within the HEYEX 2 platform via Heidelberg AppWay. This setup enables real-time gene prediction directly from multimodal SPECTRALIS scans—bringing AI-assisted diagnosis one step closer to clinical routine.
For further information visit www.eye2gene.com
