Diagnosing Pathologic Myopia by Identifying Posterior Staphyloma and Myopic Maculopathy Using Ultra-Widefield Images with Deep Learning

Diagnosing Pathologic Myopia by Identifying Posterior Staphyloma and Myopic Maculopathy Using Ultra-Widefield Images with Deep Learning | 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 Diagnosing Pathologic Myopia by Identifying Posterior Staphyloma and Myopic Maculopathy Using Ultra-Widefield Images with Deep Learning Peiwu Qin, Yang Liu, Keming Zhao, Lihui Luo, Ziheng Zhang, Zhenghang Qian, and 18 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5421907/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 13 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted 10 You are reading this latest preprint version Abstract Pathologic myopia (PM) has long been a leading cause of visual impairment and blindness. While numerous deep learning-based approaches have improved the efficiency and accuracy of recognizing PM, few have thoroughly investigated clinically significant pathological patterns due to the scarcity of datasets with lesion-wise labeling, particularly those comprising ultra-widefield (UWF) images that encompass a broader retinal field of view. In this study, we gather a large-scale multi-source ultra-widefield imaging myopia dataset, PSMM, labeled with posterior staphyloma (PS) and myopic maculopathy (MM). Compared with traditional colored fundus photography, UWF images exhibit informative characteristics concerning peripheral lesions caused by axial elongation and structural deformation in eyes with pathologic myopia. The labels obtained from the dataset can substantially assist in the progression diagnosis of pathologic myopia and guide prognosis. We introduce an end-to-end lightweight framework called RealMNet, which precisely identifies these challenging pathological patterns underpinned by a well-curated dataset. RealMNet is more adaptable to medical devices with only 21 million parameters compared to existing approaches. Through extensive experiments on a unified platform using all-around metrics regarding bipartitions and rankings across three experimental protocols, we demonstrate the robustness and generalizability of RealMNet, showcasing promising merit in clinical applications. Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Published Journal Publication published 13 Jul, 2025 Read the published version in npj Digital Medicine → Version 1 posted Editorial decision: Revision requested 22 Dec, 2024 Reviews received at journal 21 Dec, 2024 Reviewers agreed at journal 16 Dec, 2024 Reviewers agreed at journal 15 Dec, 2024 Reviews received at journal 07 Dec, 2024 Reviewers agreed at journal 26 Nov, 2024 Reviewers invited by journal 20 Nov, 2024 Editor assigned by journal 12 Nov, 2024 Submission checks completed at journal 12 Nov, 2024 First submitted to journal 09 Nov, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. 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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-5421907","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":382839201,"identity":"9ff95eb1-612f-49e5-8031-1b2206c57ac7","order_by":0,"name":"Peiwu 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While numerous deep learning-based approaches have improved the efficiency and accuracy of recognizing PM, few have thoroughly investigated clinically significant pathological patterns due to the scarcity of datasets with lesion-wise labeling, particularly those comprising ultra-widefield (UWF) images that encompass a broader retinal field of view. In this study, we gather a large-scale multi-source ultra-widefield imaging myopia dataset, PSMM, labeled with posterior staphyloma (PS) and myopic maculopathy (MM). Compared with traditional colored fundus photography, UWF images exhibit informative characteristics concerning peripheral lesions caused by axial elongation and structural deformation in eyes with pathologic myopia. The labels obtained from the dataset can substantially assist in the progression diagnosis of pathologic myopia and guide prognosis. 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