Deep Neural Networks for the Early Prediction of Abnormal Blood Flow: A Systematic Review of Techniques, Clinical Validation, and Future Directions

Deep Neural Networks for the Early Prediction of Abnormal Blood Flow: A Systematic Review of Techniques, Clinical Validation, and Future Directions | 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 Deep Neural Networks for the Early Prediction of Abnormal Blood Flow: A Systematic Review of Techniques, Clinical Validation, and Future Directions Ahmed A.Mageed¹, Ahmed Y.Khedr¹, Yusuf Nassar², Talaat Abdelhamid³, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-9282596/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 12 You are reading this latest preprint version Abstract Blood flow dynamics and their abnormalities are pivotal in both cardiovascular and cerebrovascular diseases. Detecting these early is challenging, primarily because traditional methods are often invasive, resource-intensive, or inadequate in prediction. Deep neural networks (DNNs) offer a promising alternative, capable of interpreting complex, nonlinear patterns in high-dimensional medical imaging and physiological data, thus providing a non-invasive means to anticipate potential issues. In our systematic review, we thoroughly examined existing DNN methods aimed at early prediction of these problematic flow states. We focused on aspects such as model architectures, data types, testing methodologies, and their applicability in real-world scenarios. Our search through major biomedical and engineering databases identified 192 studies that met our criteria, encompassing a variety of architectures, including convolutional, recurrent, transformer, and physics-informed models. The field is predominantly led by CNN-based models, which constitute 34.9% of the studies, primarily targeting image-based tasks. In contrast, recurrent and transformer models appear in only 2.1% and 6.3% of the studies, despite their proficiency in handling temporal flow. Physics-informed models serve as a bridge, offering enhanced interpretability and physiological alignment. However, 44.3% of studies fail to specify their DNN architecture. While these models demonstrate strong internal performance, external validation is limited to just 16.7% of studies. Real-world clinical evaluation is notably scarce, occurring in only 6.3% of the cases. The challenges are substantial, involving dataset diversity, inconsistent reporting standards, and complex model interpretability. Although DNNs are promising for non-invasive hemodynamic predictions their transition from laboratory to clinical practice is hindered by insufficient external validation reporting inconsistencies and interpretability issues. Future efforts should focus on multi-center prospective studies, the utilization of explainable AI, and the development of standardized datasets to enable DNNs to realize their potential in clinical applications. Deep neural networks Hemodynamics Blood flow prediction Systematic review Clinical validation Convolutional neural network Full Text Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterialDeepNeuralNetworksfortheEarlyPredictionofAbnormalBloodFlowv83.docx Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Revision requested 11 May, 2026 Reviews received at journal 09 May, 2026 Reviews received at journal 20 Apr, 2026 Reviewers agreed at journal 19 Apr, 2026 Reviewers agreed at journal 14 Apr, 2026 Reviews received at journal 13 Apr, 2026 Reviewers agreed at journal 13 Apr, 2026 Reviewers agreed at journal 12 Apr, 2026 Reviewers invited by journal 10 Apr, 2026 Editor assigned by journal 09 Apr, 2026 Submission checks completed at journal 08 Apr, 2026 First submitted to journal 31 Mar, 2026 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. 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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-9282596","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Systematic Review","associatedPublications":[],"authors":[{"id":624809427,"identity":"4a7a20c8-8717-4663-8427-b26e30a568a5","order_by":0,"name":"Ahmed 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