Computer Vision for Rare Diseases: A Scoping Review

Computer Vision for Rare Diseases: A Scoping Review | 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 Systematic Review Computer Vision for Rare Diseases: A Scoping Review Zhichuan Xu, Jie Song, Cheng Bi, Yuxin Zhang, Xin Zheng, Meng Xiao, and 3 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8109549/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Rare diseases, though individually infrequent, collectively affect over 300 million people worldwide. The clinical heterogeneity, delayed diagnosis, and limited expertise associated with rare diseases present substantial challenges for early detection and effective management. Computer Vision (CV) technologies, by analyzing medical images (such as X-rays and MRI), pathological slides, facial features, and other physical manifestations, demonstrate significant potential in various clinical aspects of rare diseases, including automated screening, assisted diagnosis, subtyping, monitoring, surgical support, and prognostic assessment. However, the landscape of CV research in rare diseases remains fragmented, with limited overviews of available datasets, state-of-the-art technologies, and clinical applications. In this scoping review, we provide a comprehensive overview of CV research in rare diseases, identifying and analyzing 772 relevant publications. We provide a detailed summary of publicly available datasets, discuss recent advances in CV methodologies—including data augmentation, interpretability, multimodal fusion, training strategies, and loss function design. Furthermore, we categorize and review clinical applications across major rare disease types, including ophthalmic, neurologic, developmental, skeletal, respiratory, genitourinary, cardiovascular, hematologic, rheumatologic, and endocrine/metabolic diseases. We also discuss key challenges hindering the widespread adoption of CV in clinical practice, such as data privacy, fairness, scarcity and imbalance, annotation quality, interpretability, and computing resource constraints, and propose possible future directions, including multi-disciplinary collaboration, privacy-preserving data sharing, domain adaptation, multimodal fusion, advanced interpretability, and efficient model deployment. This scoping review aims to provide a comprehensive reference for researchers and clinicians by systematically mapping the landscape of CV research in rare diseases, thereby facilitating the advancement of CV-driven diagnosis and management, and promoting the real-world clinical translation of these technologies in intelligent medicine for rare diseases. Nuclear Medicine & Medical Imaging Artificial Intelligence and Machine Learning Rare Diseases Computer Vision Medical Imaging Full Text Additional Declarations The authors declare no competing interests. Cite Share Download PDF Status: Posted Version 1 posted 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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