DINO-EYE: Self-Supervised Learning for Identification of Different Optic Disc Phenotypes in Primary Open Angle Glaucoma

DINO-EYE: Self-Supervised Learning for Identification of Different Optic Disc Phenotypes in Primary Open Angle Glaucoma | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article DINO-EYE: Self-Supervised Learning for Identification of Different Optic Disc Phenotypes in Primary Open Angle Glaucoma Lourdes Grassi, Zhe Fei, Esteban Morales, Joseph Caprioli This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7237712/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 10 Jan, 2026 Read the published version in Scientific Reports → Version 1 posted 11 You are reading this latest preprint version Abstract Purpose To develop a self-supervised learning (SSL) model that classifies optic disc phenotypes in primary open angle glaucoma (POAG) and explores novel phenotypic patterns with optic disc photographs (ODPs). Methods We collected 850 ODPs from patients with POAG and applied data augmentation to address class imbalances, yielding 10,493 images. Using the DINO Vision Transformer as the backbone model, we trained an SSL model to extract 2048-dimensional latent features. These features were used for both supervised classification of six known phenotypes and unsupervised clustering. Classification performance was evaluated with Random Forest and XGBoost models. UMAP was used for dimensionality reduction and feature visualization, and attention maps were generated for model interpretability. Results The DINO-Eye model features enabled phenotype classification with 91% accuracy with Random Forest and 92.1% after merging clinically similar phenotypes. Unsupervised clustering revealed coherent groupings, particularly for concentric thinning and Extensive PPA, though no new phenotypes were unanimously confirmed by clinicians. The proposed model outperformed the RETFound SSL model in phenotype classification and demonstrated interpretable attention regions consistent with expert criteria. Conclusion Our DINO-Eye effectively extracts clinically meaningful features from fundus images and enables accurate classification of optic disc phenotypes in POAG. It surpasses existing SSL models in performance and interpretability, offering promise for real-world glaucoma decision support and individualized care planning. Biological sciences/Computational biology and bioinformatics Health sciences/Diseases Health sciences/Medical research Primary Open-Angle Glaucoma Self-Supervised Learning Optic Disc Photograph Vision Transformer Fundus Photography Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 Introduction Glaucoma is a chronic progressive optic neuropathy characterized by damage to the optic nerve head and is a leading cause of irreversible blindness worldwide 1 . A recent meta-analysis predicted that by 2040, glaucoma will impact around 111.8 million people 2 . Early detection is a critical component of treatment and disease progression prevention. Various diagnostic tools are used for glaucoma detection, including Optic Disc Photographs (ODPs), a traditional and relatively inexpensive option for the screening and monitoring of patients. The employment of ODPs in clinical settings led to the classification of Primary Open Angle Glaucoma (POAG) according to the optic disc appearance or phenotype 3 . A recent classification based on phenotypic expression includes: generalized thinning, focal thinning, acquired pit of the optic nerve (APON), tilted, extensive peripapillary atrophy, and broad thinning 4 . These phenotypes are associated with specific demographics as well as clinical and ophthalmological characteristics, helping to individualize treatment 5 . Although this phenotypic classification can be applied generally, agreement among clinicians proves to be challenging due to interpersonal disagreements, biases, differing personal experiences, and levels of training. Grassi et al. 4 previously reported a 40% disagreement rate between two graders, and even when a third grader was introduced as a tiebreaker, there remained a 30% rate of disagreement among all three experts. With the rapid emergence of deep learning, particularly self-supervised learning (SSL), new glaucoma diagnostic tools are being developed to enhance accuracy and efficiency, especially as it pertains to interpretation of ODPs 6 – 11 , . These developments may improve accuracy for diagnosis; this is relevant since glaucoma is a silent disease that can remain undiagnosed for years until it progresses to a severe stage. Artificial Intelligence (AI) algorithms, including Convolutional Neural Networks (CNNs) and Vision Transformers, are trained with large datasets of high-quality images. Recent studies examining the relationship between the optic nerve disc and rim for glaucoma screening and progression detection have demonstrated the importance of developing these AI models 9 , 12 – 15 , . However, most AI applications in glaucoma have been developed on binary classification (e.g., glaucoma vs non-glaucoma), rather than a more difficult, finer phenotypic differentiation. In this study, we developed an SSL-based deep learning model (DINO-Eye) with a large dataset of ODPs to predict existing optic disc phenotypes and to identify new variations in patients with POAG. Our model applies the DINO Vision Transformer framework to learn representations of the optic disc morphology. These representations are then used for supervised classification of known phenotypes, as well as unsupervised clustering to explore potential novel phenotypic patterns to better understand the pathophysiologic diversity in glaucoma patients. Methods This retrospective study was conducted using deidentified patient data at the Stein Eye Institute of the University of California, Los Angeles (UCLA); adheres to the tenets of the Declaration of Helsinki; was approved by the UCLA Human Research Protection Program; and conforms to Health Insurance Portability and Accountability Act (HIPAA) policies. Informed consent to participate was waived as approved by the UCLA Bruin Institutional Review Board (IRB). A total of 850 ODPs were obtained from the UCLA Stein Eye Institute Glaucoma database. The inclusion criteria were eyes with POAG diagnosis, Median Deviation (MD) greater than − 10 dB, and optic disc area between 1–3 mm² closest to the date of ODP. To address class imbalance among phenotypes, we applied offline augmentation of the original images. The original phenotype frequencies and the augmentation ratios were reported in Table 1 , where the augmentation ratios are approximately inversely proportional to the frequencies. The augmentations were randomly assigned to be rotation, zooming, and adding Gaussian noise. The total number of augmented images is N = 10493, and the augmented sample sizes for each phenotype are shown in the last column of Table 1 . Table 1 Phenotype frequency and augmentation ratios of the 850 glaucoma eyes. Phenotype Count Augmentation Ratio Augmented Count APON 25 15 1525 Broad Thinning 150 3 1964 Concentric Thinning 141 3 1806 Extensive PPA 45 8 1525 Focal Thinning 384 1 1908 Tilted 105 4 1765 SSL models were developed to extract high level features/representations of the original images, which can be used for downstream tasks, like predicting phenotypes of interest. We used the DINO (self-distillation with no labels) Vision Transformer 16 as the backbone model (Fig. 1 ), which uses a teacher–student (self-distillation) framework and generates different views of the same input image with augmentations (see also BYOL 17 ) to learn the common and distinct features from these views (Fig. 2 ). 80% of the entire augmented data was used to train the DINO model. Then, the outputs of the DINO model, 2048-dimensional feature vectors, serve as high-level latent representations of potential clinical characteristics of the original images. Subsequent analyses were performed on these high-level features learned from the DINO model. Specifically, the extracted features were used as input for supervised phenotype classification. Two ensemble-based classifiers, Random Forest and XGBoost, were trained on the held-out 20% augmented dataset consisting of 2,209 samples. An 80 − 20 train-test split was further applied, ensuring class balance within both sets. Performance was evaluated by measuring accuracy on the test subset. To explore potential novel phenotypes, we applied unsupervised clustering to the 2048-dimensional latent features. Several clustering methods were performed with a target of 5 clusters, including K-means, Agglomerative Clustering (Agglo), and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH). Three experienced clinicians reviewed representative samples from each cluster to assess whether novel phenotypic patterns could be identified. UMAP (Uniform Manifold Approximation and Projection) was used to reduce feature dimensionality for visualization, highlighting several coherent clusters. Lastly, to gain insight into the regions of the images that most strongly influenced model predictions, we generated class-specific attention maps. These visualizations allowed for qualitative evaluation of model focus across different phenotypes. The attention maps can potentially highlight clinically meaningful features, offering reassurance that the DINO model’s internal representations aligned with expert-driven diagnostic criteria. Results Model Training and Latent Feature Extraction We used a batch size of 32, the pretrained “dino_vits16” as the backbone model with input embedding dimension 384, a learning rate of 0.001, a maximum epoch of 250, and an early stopping criterion of patience = 50. The DINO-Eye SSL model successfully learned 2048 features from the original input images. The latent features were visualized with Uniform Manifold Approximation and Projection (UMAP) for dimension reduction. Phenotype Classification Using SSL Features We further applied random forest and XGboost for phenotype classification using these features, on the held out augmented dataset (N = 2099). With an 80 − 20 training test split, we achieved an accuracy of 91% to identify the true phenotypes on the test set with random forest, and accuracy of 90.5% with XGboost (Fig. 3 ). Since APON and focal thinning are two clinically similar phenotypes, we combined them into one and repeated the classifications. This classification improved to 92.1% accuracy with random forest (Fig. 4 ). Clustering for Phenotype Matching and Novel Phenotype Discovery We also performed clustering on the latent features to look for novel phenotypes. To ensure a comprehensive exploration of clustering behavior, we employed three different clustering algorithms: K-means, Agglomerative Clustering, and BIRCH. By comparing results across these methods, we were able to assess the stability and consistency of the phenotypic groupings. Among the three, Agglomerative Clustering produced the most clinically coherent groupings and demonstrated the highest alignment with the clinically assigned phenotype labels (Table 2). Several clusters showed high internal consistency, particularly for concentric thinning and extensive PPA. However, considerable overlap was observed among other phenotypes, suggesting areas for re-evaluation of phenotype boundaries. Three glaucoma-trained clinicians checked each different cluster to find new phenotypes. We found that among concentric thinning cases, 35.5% and 37.6% agree with cluster 0 and 1, respectively. Among extensive peripapillary atrophy, 53.3% agree with cluster 2. These phenotypes showed the most alignment with the model learned clusters. Although there were remarkable characteristics in each cluster, no new phenotype was confirmed by the examiners. Table 2. Comparing Agglomerative clusters with true phenotypes, with counts (row percentage) reported in each cell. Cells with high percentages of agreement were bolded. Attention Map Interpretability We created attention maps of the images by each phenotype to better understand what features the DINO model learned that are clinically meaningful and can be matched to the phenotype. Overall, attention heads 0 and 5 do not appear to be related to any phenotypes. In the given examples, attention head 1 reveals feature related to the tilted phenotype; attention head 2 is related to extensive peripapillary atrophy; attention head 3 is related to concentric thinning; and attention head 4 is related to broad thinning. Comparison with RETFound Model Lastly, we applied the RETFound 20 pretrained model to our data for comparison and external validation. The pretrained RETFound model was fine-tuned on our data and learned 1024 latent features for subsequent analyses. Phenotype classification with random forest was performed with these latent features on the same 2099 held-out augmentation samples. The best performance achieved 0.89 test accuracy, which was slightly worse than the DINO-Eye model. Discussion In this study, we developed the DINO-Eye SSL model that can identify different phenotypes in patients with POAG using a unique dataset of 850 ODPs. Building on prior classifications by Nicolela and Drance 3 , and Grassi et al. 4 , our model addresses the limitations posed by inter-observer variability and subjective bias in phenotype classification. The SSL machine for identification of the different phenotypes offers an alternative to reduce human error in the classification. The RETFound model 20 , a SSL foundation model that was trained with a large number of images (1.6 million of unlabelled data), was also applied to our data to learn generalizable representations from retinal images. However, this more general model did not show superior performance on our dataset, suggesting a limitation in the generalizability of such models. Notably, we highlight the interpretability of our model through attention maps and UMAP clustering, key to clinician relevance and common usage. The proposed DINO-Eye achieves the best performance with an accuracy of 92.1% in identifying the different phenotypes, based on 850 images from a previous study. In comparison, Benton Chuter et al. 21 , offered an Artificial Intelligence mode using RETFound to classify ODP in “Glaucomatous” or “Non-glaucomatous”. With fine-tuning on the same dataset, RETFound only achieved an accuracy of 89% on the same phenotype classification task. This comparison highlights the importance of adapting large-scale pretrained models to specific clinical tasks through fine-tuning, which can significantly improve performance even with a smaller dataset. Despite promising classification performance, our attempt to identify novel phenotypes through unsupervised clustering did not yield clearly new categories. Nevertheless, the coherent groupings observed suggest that further exploration with larger and more diverse datasets could refine existing subclassifications or reveal subtle phenotypic variants. Notably, some clusters demonstrated a dominant presence of specific phenotypes. For example, concentric thinning, tilted and extensive peripapillary atrophy were often grouped consistently. This could be due to the strong, remarkable characteristics those subtypes have. In contrast, broad thinning, focal thinning and APON exhibited overlap with multiple clusters, possibly reflecting unapparent differences. Key limitations should be acknowledged. First, our dataset, although augmented, originated from a single clinical center and included only 850 original images, which may limit the model's generalizability. Multi-center validation with demographically diverse populations is essential to confirm robustness. Second, the phenotype labels used in supervised classification were derived from expert grading, which can be inherently subjective. Additionally, although the model’s attention maps provide interpretability, the risk of “data fishing” remains. Future directions include: i) expanding the dataset with other collections worldwide; ii) experimenting with other augmentation techniques to address imbalance classes more effectively; iii) incorporate feature selection of SSL latent features that are specific to clinical outcomes, such as progression analysis or treatment response. We see this model being used for decision support in the real-world, providing an unbiased opinion to non-glaucoma specialists or for training purposes. Our study is among the few that apply SSL to the more challenging problem of glaucoma phenotyping. Other recent SSL applications in ophthalmology have largely focused on disease detection or segmentation tasks 22 – 24 . Related works include PaRCL 25 , a self-supervised contrastive learning method for glaucoma classification using fundus images; a semi-supervised learning 26 method for retinal fundus image segmentation via self-training based on the MR-Net; One-Vote Veto (OVV) self-training 27 , a semi-supervised learning strategy for glaucoma diagnosis using unlabeled fundus images. By contrast, SSL methods on phenotypes are scarce. Our focus on phenotype-level classification opens pathways for individualized treatment planning. Conclusion We developed DINO-Eye, a self-supervised learning framework that successfully extracted meaningful features from ODPs. We used the SSL feature to accurately classify clinically assigned optic disc phenotypes in POAG. The DINO-Eye model achieved phenotype classification accuracy of 90% or higher. The model outperforms the existing state-of-art approach RETFound on the same dataset and has the potential to reduce clinician subjectivity in phenotype classification. However, no new phenotypes were confirmed by clinicians in this study. Further validation and scaling are warranted for clinical deployment and discovery of novel disease subtypes. Declarations Funding/Support: Dr. Caprioli received grants from the Simms/Mann Family Foundation, the Payden Memorial Foundation, and Research to Prevent Blindness. Role of the Funder/Sponsor: The funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication. Disclosure : Lourdes Grassi, None; Zhe Fei, None; Esteban Morales, None; Joseph Caprioli, None Competing interests The author(s) declare no competing interests. Author Contribution L.G., E.M., Z.F., and J.C. conceived the study design. L.G. and E.M. collected the clinical data. Z.F. developed the DINO-Eye model architecture. E.M. and Z.F. performed the statistical analyses. L.G. and Z.F. wrote the main manuscript text. E.M., Z.F., and L.G. prepared all figures. J.C. provided clinical expertise and supervised the project. All authors contributed equally to the interpretation of results, manuscript revision, and approved the final version. Data Availability The datasets generated during and/or analysed during the current study are not publicly available due to patient privacy. For more information, please contact the corresponding author, Joseph Caprioli, Jules Stein Eye Institute, Los Angeles, CA 90095, USA; [email protected] . References Quigley, H. & Broman, A. T. The number of people with glaucoma worldwide in 2010 and 2020. Br. J. Ophthalmol. 90 (3), 262–267. 10.1136/BJO.2005.081224 (2006). Tham, Y. C. et al. Global Prevalence of Glaucoma and Projections of Glaucoma Burden through 2040: A Systematic Review and Meta-Analysis. Ophthalmology 121 (11), 2081–2090. 10.1016/J.OPHTHA.2014.05.013 (2014). Nicolela, M. T. & Drance, S. M. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7237712","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":502587987,"identity":"d4dcda8a-6e9f-4532-a45b-3709ec80c188","order_by":0,"name":"Lourdes Grassi","email":"","orcid":"","institution":"University of California Los Angeles (UCLA)","correspondingAuthor":false,"prefix":"","firstName":"Lourdes","middleName":"","lastName":"Grassi","suffix":""},{"id":502587988,"identity":"6230b6a2-6b50-4883-8bd6-aa7ed2bbe40a","order_by":1,"name":"Zhe Fei","email":"","orcid":"","institution":"UC Riverside","correspondingAuthor":false,"prefix":"","firstName":"Zhe","middleName":"","lastName":"Fei","suffix":""},{"id":502587989,"identity":"7a4e4aa4-5d2c-4ebe-8cd9-2cd14cc3c2fb","order_by":2,"name":"Esteban Morales","email":"","orcid":"","institution":"University of California Los Angeles (UCLA)","correspondingAuthor":false,"prefix":"","firstName":"Esteban","middleName":"","lastName":"Morales","suffix":""},{"id":502587990,"identity":"3c04c5cb-cc77-4681-9779-ebe742331230","order_by":3,"name":"Joseph Caprioli","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA+klEQVRIiWNgGAWjYBAC/gYgkcBgwcPPcADEYGDgAwnz4NEicQCsRYJHsgGqhY2QFgMHiE4GgwNQEcJa2M8e+/CgQkLG+ODxhw8eMByWZ2M/wPjgbRseLTx5yTMSzkjwmB04Y2yQwHDYsI0ngdlwLj4tDDnGDIltYC1sEkAtCWwMCWzSvPi08L+BaDFuOP78B1gL/wP233i1REBtMWA4YMYA1iKRwMaMT4vEjXfJDCC/SAD9IpFgkG7YJvGwWXLOOdxa+PtzDzP+qLCx559x/OHHHxXW8vz8yQc/vCnDrQURBeBINQCxGBvwqUfSwk9I4SgYBaNgFIxYAACKHEtaPLjHcwAAAABJRU5ErkJggg==","orcid":"","institution":"University of California Los Angeles (UCLA)","correspondingAuthor":true,"prefix":"","firstName":"Joseph","middleName":"","lastName":"Caprioli","suffix":""}],"badges":[],"createdAt":"2025-07-29 00:53:10","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7237712/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7237712/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-33140-1","type":"published","date":"2026-01-10T15:58:11+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":89564491,"identity":"e7d406a5-dbc2-4ad9-89b6-78ba750df75b","added_by":"auto","created_at":"2025-08-21 10:37:09","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":88696,"visible":true,"origin":"","legend":"\u003cp\u003eDINO ViT model overview, left is Self-distillation with teacher-student architecture\u003csup\u003e18\u003c/sup\u003e; right is the Vision Transformer architecture\u003csup\u003e19\u003c/sup\u003e.\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/8c2394728650442e52b69c0e.png"},{"id":89565471,"identity":"49b4ad56-cb71-4199-b552-f2e6e8cebd87","added_by":"auto","created_at":"2025-08-21 10:45:09","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":300711,"visible":true,"origin":"","legend":"\u003cp\u003e8-view panels of the same glaucoma image by DINO backbone model, including 2 global crops and 6 local crops that focus on different parts of the image.\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/7ddf9dd8147cdbc979aa0f61.png"},{"id":89565470,"identity":"976fa90f-fca3-4663-b3eb-91112408d3c7","added_by":"auto","created_at":"2025-08-21 10:45:09","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":61226,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotype classification using 2048 SSL features on the held-out augmented dataset. Confusion matrices of 420 test images are reported.\u003c/p\u003e","description":"","filename":"3.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/da1eaa69ae59c2179f6d830d.png"},{"id":89563063,"identity":"6448477c-f628-4dc0-a60f-97142b6c60e0","added_by":"auto","created_at":"2025-08-21 10:29:09","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":30806,"visible":true,"origin":"","legend":"\u003cp\u003ePhenotype classification using 2048 SSL features and 5 phenotypes (APON and focal thinning combined).\u003c/p\u003e","description":"","filename":"4.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/eeb44811d90166ff7d7d7718.png"},{"id":89561014,"identity":"d698e4dc-47aa-40f6-8d4c-b5c0754f1b0c","added_by":"auto","created_at":"2025-08-21 10:21:09","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":83020,"visible":true,"origin":"","legend":"\u003cp\u003eUMAP visualization of DINO latent features. Clustering was based on the K-means method.\u003c/p\u003e","description":"","filename":"5.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/cf74bf289c595eea7457d398.png"},{"id":89561024,"identity":"f3be5e3f-9867-4f6f-a62e-1ccce2115bca","added_by":"auto","created_at":"2025-08-21 10:21:10","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":287814,"visible":true,"origin":"","legend":"\u003cp\u003eAttention head maps for each phenotype. The clinical characteristic pattern is circled in white.\u003c/p\u003e","description":"","filename":"6.png","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/e20ac523723be7cdc9d0e0e2.png"},{"id":100069285,"identity":"75d9a8fe-c0da-410e-b5bb-77bcc08ae6a8","added_by":"auto","created_at":"2026-01-12 16:12:37","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1684127,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7237712/v1/137c8ee2-426e-4db9-9a9e-981b6d3bf9bd.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"DINO-EYE: Self-Supervised Learning for Identification of Different Optic Disc Phenotypes in Primary Open Angle Glaucoma","fulltext":[{"header":"Introduction","content":"\u003cp\u003eGlaucoma is a chronic progressive optic neuropathy characterized by damage to the optic nerve head and is a leading cause of irreversible blindness worldwide\u003csup\u003e\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e\u003c/sup\u003e. A recent meta-analysis predicted that by 2040, glaucoma will impact around 111.8\u0026nbsp;million people\u003csup\u003e\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e\u003c/sup\u003e. Early detection is a critical component of treatment and disease progression prevention.\u003c/p\u003e\u003cp\u003eVarious diagnostic tools are used for glaucoma detection, including Optic Disc Photographs (ODPs), a traditional and relatively inexpensive option for the screening and monitoring of patients. The employment of ODPs in clinical settings led to the classification of Primary Open Angle Glaucoma (POAG) according to the optic disc appearance or phenotype\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e. A recent classification based on phenotypic expression includes: generalized thinning, focal thinning, acquired pit of the optic nerve (APON), tilted, extensive peripapillary atrophy, and broad thinning\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e. These phenotypes are associated with specific demographics as well as clinical and ophthalmological characteristics, helping to individualize treatment\u003csup\u003e\u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u003c/sup\u003e. Although this phenotypic classification can be applied generally, agreement among clinicians proves to be challenging due to interpersonal disagreements, biases, differing personal experiences, and levels of training. Grassi et al.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e previously reported a 40% disagreement rate between two graders, and even when a third grader was introduced as a tiebreaker, there remained a 30% rate of disagreement among all three experts.\u003c/p\u003e\u003cp\u003eWith the rapid emergence of deep learning, particularly self-supervised learning (SSL), new glaucoma diagnostic tools are being developed to enhance accuracy and efficiency, especially as it pertains to interpretation of ODPs\u003csup\u003e\u003cspan additionalcitationids=\"CR7 CR8 CR9 CR10\" citationid=\"CR6\" class=\"CitationRef\"\u003e6\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e,\u003c/sup\u003e. These developments may improve accuracy for diagnosis; this is relevant since glaucoma is a silent disease that can remain undiagnosed for years until it progresses to a severe stage. Artificial Intelligence (AI) algorithms, including Convolutional Neural Networks (CNNs) and Vision Transformers, are trained with large datasets of high-quality images. Recent studies examining the relationship between the optic nerve disc and rim for glaucoma screening and progression detection have demonstrated the importance of developing these AI models\u003csup\u003e\u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e,\u003cspan additionalcitationids=\"CR13 CR14\" citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e,\u003c/sup\u003e. However, most AI applications in glaucoma have been developed on binary classification (e.g., glaucoma vs non-glaucoma), rather than a more difficult, finer phenotypic differentiation.\u003c/p\u003e\u003cp\u003eIn this study, we developed an SSL-based deep learning model (DINO-Eye) with a large dataset of ODPs to predict existing optic disc phenotypes and to identify new variations in patients with POAG. Our model applies the DINO Vision Transformer framework to learn representations of the optic disc morphology. These representations are then used for supervised classification of known phenotypes, as well as unsupervised clustering to explore potential novel phenotypic patterns to better understand the pathophysiologic diversity in glaucoma patients.\u003c/p\u003e"},{"header":"Methods","content":"\u003cp\u003eThis retrospective study was conducted using deidentified patient data at the Stein Eye Institute of the University of California, Los Angeles (UCLA); adheres to the tenets of the Declaration of Helsinki; was approved by the UCLA Human Research Protection Program; and conforms to Health Insurance Portability and Accountability Act (HIPAA) policies. Informed consent to participate was waived as approved by the UCLA Bruin Institutional Review Board (IRB).\u003c/p\u003e\u003cp\u003eA total of 850 ODPs were obtained from the UCLA Stein Eye Institute Glaucoma database. The inclusion criteria were eyes with POAG diagnosis, Median Deviation (MD) greater than \u0026minus;\u0026thinsp;10 dB, and optic disc area between 1\u0026ndash;3 mm\u0026sup2; closest to the date of ODP.\u003c/p\u003e\u003cp\u003eTo address class imbalance among phenotypes, we applied offline augmentation of the original images. The original phenotype frequencies and the augmentation ratios were reported in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e, where the augmentation ratios are approximately inversely proportional to the frequencies. The augmentations were randomly assigned to be rotation, zooming, and adding Gaussian noise. The total number of augmented images is N\u0026thinsp;=\u0026thinsp;10493, and the augmented sample sizes for each phenotype are shown in the last column of Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e.\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003ePhenotype frequency and augmentation ratios of the 850 glaucoma eyes.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"4\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePhenotype\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCount\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eAugmentation Ratio\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003eAugmented Count\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAPON\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e25\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e15\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eBroad Thinning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e150\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1964\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eConcentric Thinning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e141\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1806\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eExtensive PPA\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e45\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1525\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eFocal Thinning\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e384\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1908\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eTilted\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e\u003cp\u003e105\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e\u003cp\u003e1765\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eSSL models were developed to extract high level features/representations of the original images, which can be used for downstream tasks, like predicting phenotypes of interest. We used the DINO (self-distillation with no labels) Vision Transformer\u003csup\u003e\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u003c/sup\u003e as the backbone model (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e), which uses a teacher\u0026ndash;student (self-distillation) framework and generates different views of the same input image with augmentations (see also BYOL\u003csup\u003e\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e\u003c/sup\u003e) to learn the common and distinct features from these views (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). 80% of the entire augmented data was used to train the DINO model.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eThen, the outputs of the DINO model, 2048-dimensional feature vectors, serve as high-level latent representations of potential clinical characteristics of the original images. Subsequent analyses were performed on these high-level features learned from the DINO model. Specifically, the extracted features were used as input for supervised phenotype classification. Two ensemble-based classifiers, Random Forest and XGBoost, were trained on the held-out 20% augmented dataset consisting of 2,209 samples. An 80\u0026thinsp;\u0026minus;\u0026thinsp;20 train-test split was further applied, ensuring class balance within both sets. Performance was evaluated by measuring accuracy on the test subset.\u003c/p\u003e\u003cp\u003eTo explore potential novel phenotypes, we applied unsupervised clustering to the 2048-dimensional latent features. Several clustering methods were performed with a target of 5 clusters, including K-means, Agglomerative Clustering (Agglo), and Balanced Iterative Reducing and Clustering using Hierarchies (BIRCH). Three experienced clinicians reviewed representative samples from each cluster to assess whether novel phenotypic patterns could be identified. UMAP (Uniform Manifold Approximation and Projection) was used to reduce feature dimensionality for visualization, highlighting several coherent clusters.\u003c/p\u003e\u003cp\u003eLastly, to gain insight into the regions of the images that most strongly influenced model predictions, we generated class-specific attention maps. These visualizations allowed for qualitative evaluation of model focus across different phenotypes. The attention maps can potentially highlight clinically meaningful features, offering reassurance that the DINO model\u0026rsquo;s internal representations aligned with expert-driven diagnostic criteria.\u003c/p\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003eModel Training and Latent Feature Extraction\u003c/h2\u003e\u003cp\u003eWe used a batch size of 32, the pretrained \u0026ldquo;dino_vits16\u0026rdquo; as the backbone model with input embedding dimension 384, a learning rate of 0.001, a maximum epoch of 250, and an early stopping criterion of patience\u0026thinsp;=\u0026thinsp;50. The DINO-Eye SSL model successfully learned 2048 features from the original input images. The latent features were visualized with Uniform Manifold Approximation and Projection (UMAP) for dimension reduction.\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ePhenotype Classification Using SSL Features\u003c/h3\u003e\n\u003cp\u003eWe further applied random forest and XGboost for phenotype classification using these features, on the held out augmented dataset (N\u0026thinsp;=\u0026thinsp;2099). With an 80\u0026thinsp;\u0026minus;\u0026thinsp;20 training test split, we achieved an accuracy of 91% to identify the true phenotypes on the test set with random forest, and accuracy of 90.5% with XGboost (Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e). Since APON and focal thinning are two clinically similar phenotypes, we combined them into one and repeated the classifications. This classification improved to 92.1% accuracy with random forest (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e).\u003c/p\u003e\n\u003ch3\u003eClustering for Phenotype Matching and Novel Phenotype Discovery\u003c/h3\u003e\n\u003cp\u003eWe also performed clustering on the latent features to look for novel phenotypes. To ensure a comprehensive exploration of clustering behavior, we employed three different clustering algorithms: K-means, Agglomerative Clustering, and BIRCH. By comparing results across these methods, we were able to assess the stability and consistency of the phenotypic groupings. Among the three, Agglomerative Clustering produced the most clinically coherent groupings and demonstrated the highest alignment with the clinically assigned phenotype labels (Table\u0026nbsp;2). Several clusters showed high internal consistency, particularly for concentric thinning and extensive PPA. However, considerable overlap was observed among other phenotypes, suggesting areas for re-evaluation of phenotype boundaries.\u003c/p\u003e\u003cp\u003eThree glaucoma-trained clinicians checked each different cluster to find new phenotypes. We found that among concentric thinning cases, 35.5% and 37.6% agree with cluster 0 and 1, respectively. Among extensive peripapillary atrophy, 53.3% agree with cluster 2. These phenotypes showed the most alignment with the model learned clusters. Although there were remarkable characteristics in each cluster, no new phenotype was confirmed by the examiners.\u003c/p\u003e\u003cp\u003eTable\u0026nbsp;2. Comparing Agglomerative clusters with true phenotypes, with counts (row percentage) reported in each cell. Cells with high percentages of agreement were bolded.\u003c/p\u003e\u003cp\u003e\u003cimg 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\" width=\"584\" height=\"423\"\u003e\u003c/p\u003e\n\u003ch3\u003eAttention Map Interpretability\u003c/h3\u003e\n\u003cp\u003eWe created attention maps of the images by each phenotype to better understand what features the DINO model learned that are clinically meaningful and can be matched to the phenotype. Overall, attention heads 0 and 5 do not appear to be related to any phenotypes. In the given examples, attention head 1 reveals feature related to the tilted phenotype; attention head 2 is related to extensive peripapillary atrophy; attention head 3 is related to concentric thinning; and attention head 4 is related to broad thinning.\u003c/p\u003e\n\u003ch3\u003eComparison with RETFound Model\u003c/h3\u003e\n\u003cp\u003eLastly, we applied the RETFound\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e pretrained model to our data for comparison and external validation. The pretrained RETFound model was fine-tuned on our data and learned 1024 latent features for subsequent analyses. Phenotype classification with random forest was performed with these latent features on the same 2099 held-out augmentation samples. The best performance achieved 0.89 test accuracy, which was slightly worse than the DINO-Eye model.\u003c/p\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, we developed the DINO-Eye SSL model that can identify different phenotypes in patients with POAG using a unique dataset of 850 ODPs. Building on prior classifications by Nicolela and Drance\u003csup\u003e\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e\u003c/sup\u003e, and Grassi et al.\u003csup\u003e\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e\u003c/sup\u003e, our model addresses the limitations posed by inter-observer variability and subjective bias in phenotype classification. The SSL machine for identification of the different phenotypes offers an alternative to reduce human error in the classification. The RETFound model\u003csup\u003e\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e\u003c/sup\u003e, a SSL foundation model that was trained with a large number of images (1.6\u0026nbsp;million of unlabelled data), was also applied to our data to learn generalizable representations from retinal images. However, this more general model did not show superior performance on our dataset, suggesting a limitation in the generalizability of such models. Notably, we highlight the interpretability of our model through attention maps and UMAP clustering, key to clinician relevance and common usage.\u003c/p\u003e\u003cp\u003eThe proposed DINO-Eye achieves the best performance with an accuracy of 92.1% in identifying the different phenotypes, based on 850 images from a previous study. In comparison, Benton Chuter et al.\u003csup\u003e\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e\u003c/sup\u003e, offered an Artificial Intelligence mode using RETFound to classify ODP in \u0026ldquo;Glaucomatous\u0026rdquo; or \u0026ldquo;Non-glaucomatous\u0026rdquo;. With fine-tuning on the same dataset, RETFound only achieved an accuracy of 89% on the same phenotype classification task. This comparison highlights the importance of adapting large-scale pretrained models to specific clinical tasks through fine-tuning, which can significantly improve performance even with a smaller dataset.\u003c/p\u003e\u003cp\u003eDespite promising classification performance, our attempt to identify novel phenotypes through unsupervised clustering did not yield clearly new categories. Nevertheless, the coherent groupings observed suggest that further exploration with larger and more diverse datasets could refine existing subclassifications or reveal subtle phenotypic variants. Notably, some clusters demonstrated a dominant presence of specific phenotypes. For example, concentric thinning, tilted and extensive peripapillary atrophy were often grouped consistently. This could be due to the strong, remarkable characteristics those subtypes have. In contrast, broad thinning, focal thinning and APON exhibited overlap with multiple clusters, possibly reflecting unapparent differences.\u003c/p\u003e\u003cp\u003eKey limitations should be acknowledged. First, our dataset, although augmented, originated from a single clinical center and included only 850 original images, which may limit the model's generalizability. Multi-center validation with demographically diverse populations is essential to confirm robustness. Second, the phenotype labels used in supervised classification were derived from expert grading, which can be inherently subjective. Additionally, although the model\u0026rsquo;s attention maps provide interpretability, the risk of \u0026ldquo;data fishing\u0026rdquo; remains. Future directions include: i) expanding the dataset with other collections worldwide; ii) experimenting with other augmentation techniques to address imbalance classes more effectively; iii) incorporate feature selection of SSL latent features that are specific to clinical outcomes, such as progression analysis or treatment response. We see this model being used for decision support in the real-world, providing an unbiased opinion to non-glaucoma specialists or for training purposes.\u003c/p\u003e\u003cp\u003eOur study is among the few that apply SSL to the more challenging problem of glaucoma phenotyping. Other recent SSL applications in ophthalmology have largely focused on disease detection or segmentation tasks\u003csup\u003e\u003cspan additionalcitationids=\"CR23\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e\u003c/sup\u003e. Related works include PaRCL\u003csup\u003e\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e\u003c/sup\u003e, a self-supervised contrastive learning method for glaucoma classification using fundus images; a semi-supervised learning\u003csup\u003e\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e\u003c/sup\u003e method for retinal fundus image segmentation via self-training based on the MR-Net; One-Vote Veto (OVV) self-training\u003csup\u003e\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e\u003c/sup\u003e, a semi-supervised learning strategy for glaucoma diagnosis using unlabeled fundus images. By contrast, SSL methods on phenotypes are scarce. Our focus on phenotype-level classification opens pathways for individualized treatment planning.\u003c/p\u003e"},{"header":"Conclusion","content":"\u003cp\u003eWe developed DINO-Eye, a self-supervised learning framework that successfully extracted meaningful features from ODPs. We used the SSL feature to accurately classify clinically assigned optic disc phenotypes in POAG. The DINO-Eye model achieved phenotype classification accuracy of 90% or higher. The model outperforms the existing state-of-art approach RETFound on the same dataset and has the potential to reduce clinician subjectivity in phenotype classification. However, no new phenotypes were confirmed by clinicians in this study. Further validation and scaling are warranted for clinical deployment and discovery of novel disease subtypes.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding/Support:\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eDr. Caprioli received grants from the Simms/Mann Family Foundation, the Payden Memorial Foundation, and Research to Prevent Blindness.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eRole of the Funder/Sponsor: \u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe funders had no role in the design and conduct of the study; collection, management, analysis, and interpretation of the data; preparation, review, or approval of the manuscript; and decision to submit the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDisclosure\u003c/strong\u003e:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003eLourdes Grassi, None; Zhe Fei, None; Esteban Morales, None; Joseph Caprioli, None\u003c/p\u003e\n\u003ch2\u003eCompeting interests\u003c/h2\u003e\u003cp\u003eThe author(s) declare no competing interests.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eL.G., E.M., Z.F., and J.C. conceived the study design. L.G. and E.M. collected the clinical data. Z.F. developed the DINO-Eye model architecture. E.M. and Z.F. performed the statistical analyses. L.G. and Z.F. wrote the main manuscript text. E.M., Z.F., and L.G. prepared all figures. J.C. provided clinical expertise and supervised the project. All authors contributed equally to the interpretation of results, manuscript revision, and approved the final version.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eThe datasets generated during and/or analysed during the current study are not publicly available due to patient privacy. For more information, please contact the corresponding author, Joseph Caprioli, Jules Stein Eye Institute, Los Angeles, CA 90095, USA; [email protected].\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eQuigley, H. \u0026amp; Broman, A. T. The number of people with glaucoma worldwide in 2010 and 2020. \u003cem\u003eBr. J. 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Imaging\u003c/em\u003e. \u003cb\u003e42\u003c/b\u003e (12), 3764\u0026ndash;3778. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1109/TMI.2023.3307689\u003c/span\u003e\u003cspan address=\"10.1109/TMI.2023.3307689\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e (2023).\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Primary Open-Angle Glaucoma, Self-Supervised Learning, Optic Disc Photograph, Vision Transformer, Fundus Photography","lastPublishedDoi":"10.21203/rs.3.rs-7237712/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7237712/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003ePurpose\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eTo develop a self-supervised learning (SSL) model that classifies optic disc phenotypes in primary open angle glaucoma (POAG) and explores novel phenotypic patterns with optic disc photographs (ODPs).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethods\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWe collected 850 ODPs from patients with POAG and applied data augmentation to address class imbalances, yielding 10,493 images. Using the DINO Vision Transformer as the backbone model, we trained an SSL model to extract 2048-dimensional latent features. These features were used for both supervised classification of six known phenotypes and unsupervised clustering. Classification performance was evaluated with Random Forest and XGBoost models. UMAP was used for dimensionality reduction and feature visualization, and attention maps were generated for model interpretability.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eResults\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe DINO-Eye model features enabled phenotype classification with 91% accuracy with Random Forest and 92.1% after merging clinically similar phenotypes. Unsupervised clustering revealed coherent groupings, particularly for concentric thinning and Extensive PPA, though no new phenotypes were unanimously confirmed by clinicians. The proposed model outperformed the RETFound SSL model in phenotype classification and demonstrated interpretable attention regions consistent with expert criteria.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConclusion\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eOur DINO-Eye effectively extracts clinically meaningful features from fundus images and enables accurate classification of optic disc phenotypes in POAG. It surpasses existing SSL models in performance and interpretability, offering promise for real-world glaucoma decision support and individualized care planning.\u003c/p\u003e","manuscriptTitle":"DINO-EYE: Self-Supervised Learning for Identification of Different Optic Disc Phenotypes in Primary Open Angle Glaucoma","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-08-21 10:21:04","doi":"10.21203/rs.3.rs-7237712/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2025-09-19T17:14:31+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-09-19T07:39:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"149324638008734158913276424971106101744","date":"2025-09-18T21:59:55+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"92776291993522831739627925161250095092","date":"2025-08-22T17:49:13+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-08-21T15:35:34+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"60837422618625309102854515519747550343","date":"2025-08-13T06:16:30+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-08-13T00:06:44+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2025-08-11T16:11:36+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2025-08-11T16:08:08+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-08-09T22:26:52+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-08-09T22:24:28+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"5ee5dc3f-dbde-4067-bc12-ad9d8dc45268","owner":[],"postedDate":"August 21st, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":53388363,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":53388364,"name":"Health sciences/Diseases"},{"id":53388365,"name":"Health sciences/Medical research"}],"tags":[],"updatedAt":"2026-01-12T16:03:34+00:00","versionOfRecord":{"articleIdentity":"rs-7237712","link":"https://doi.org/10.1038/s41598-025-33140-1","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2026-01-10 15:58:11","publishedOnDateReadable":"January 10th, 2026"},"versionCreatedAt":"2025-08-21 10:21:04","video":"","vorDoi":"10.1038/s41598-025-33140-1","vorDoiUrl":"https://doi.org/10.1038/s41598-025-33140-1","workflowStages":[]},"version":"v1","identity":"rs-7237712","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7237712","identity":"rs-7237712","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}