[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-85952-en":3,"doc-seo-85952-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":13,"seo_description":14,"update_tm":28,"read_time":29},85952,13056703019404,"Miles","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Integrating Physics-Informed Neural Networks and 3D Vascular Geometry Learning for Cerebral Aneurysm Detection and Multimodal Rupture-Risk Prediction","Cerebral aneurysms are localized dilations of intracranial arteries that can rupture and trigger subarachnoid hemorrhage, yet current risk assessment depends heavily on clinician interpretation of imaging and clinical factors. The work presents a modular framework that combines 3D vascular geometry learning with physics-informed hemodynamic descriptors and patient variables. A PointNeXt detector identifies aneurysm presence, while an unsteady physics-informed neural network predicts geometry-conditioned pressure, velocity, WSS, TAWSS, OSI, and relative residence time, then multimodal fusion estimates rupture-risk scores.","Integrating Physics-Informed Neural Networks and 3D Vascular Geometry Learning for Cerebral Aneurysm Detection and Multimodal Rupture-Risk Prediction  \nEshan Vipuil 1 and Xianqi Li2, *  \n1 West Shore Jr/Sr High School, Melbourne, FL, 32935, USA  \n2Department of Mathematics and Systems Engineering, Florida Institute of Technology, Melbourne, FL, 32901, USA  \n*[Corresponding author: xli@fit.edu](Corresponding author: xli@fit.edu)  \nAbstract  \nCerebral aneurysms are localized dilations of intracranial arteries that may rupture and cause subarachnoid hemorrhage. Current assessment relies on human interpretation of imaging and clinical risk factors, but integrating vascular shape, flow-related information, and patient-level variables into a unified quantitative model remains challenging. This study develops a modular framework for cerebral aneurysm detection and rupture-risk prediction using 3D vascular geometry learning, physics-informed hemodynamic descriptors, and clinical variables. A PointNeXt-based detector first identified aneurysm presence from vascular point clouds. For aneurysm-positive cases, an unsteady physics-informed neural network then generated geometryconditioned pressure, velocity, wall shear stress (WSS), time averaged WSS, oscillatory shear index (OSI), and relative residence time descriptors under prescribed Navier-Stokes residual and boundary-condition constraints. Multimodal models then integrated vascular morphology, physics-informed hemodynamic descriptors, and clinical variables to produce rupture-risk scores. The aneurysm detector achieved pooled out-of-fold area under the receiver operating characteristic curve (AUROC) of 0.959 and area under the precision-recall curve (AUPRC) of 0.859. For rupture-risk prediction, fixed 70/30 late fusion achieved the highest performance among evaluated models, with pooled AUROC of 0.827 and AUPRC of 0.732, exceeding all comparison models after Holm-corrected paired DeLong testing (all adjusted p \u003C 0.05) . Feature analysis identified OSI distribution, aneurysm location, radial geometry, and TAWSS descriptors as important contributors to cross-sectional rupture-risk discrimination. Together, these results provide a quantitative, multimodal strategy for case-specific aneurysm assessment.  \nKeywords: cerebral aneurysm; aneurysm rupture prediction; physics-informed neural networks; 3D point-cloud learning; computational fluid dynamics; multimodal hemodynamic modeling  \n1. Introduction  \nCerebral aneurysms are localized dilations of intracranial blood vessels that may rupture and cause subarachnoid hemorrhage, a life-threatening event associated with substantial morbidity and mortality (Mayo Clinic, 2024) . Many unruptured aneurysms remain asymptomatic and may never rupture during a patient’s lifetime, creating a difficult clinical management problem. Preventive treatment through endovascular intervention or microsurgical clipping can reduce future hemorrhage risk in selected patients, but these procedures also carry non-negligible procedural risks (Brown & Broderick, 2014; Thompson et al., 2015) . Current assessment relies on radiographic imaging modalities such as computed tomography angiography (CTA), magnetic resonance angiography (MRA), and digital subtraction angiography (DSA), together with clinician interpretation of aneurysm morphology and patient-level factors, including age, biological sex, aneurysm location, and prior history (Greving et al., 2014; Thompson et al., 2015). Although these workflows provide essential clinical information, aneurysm rupture reflects coupled effects of vascular morphology, local hemodynamics, vessel-wall properties, and patient-specific physiology. This complexity motivates quantitative approaches that can integrate complementary information sources for case-specific aneurysm assessment.  \nDeep learning (DL) has emerged as a promising data-driven approach for analyzing cerebral aneurysms from medical imaging and vascular geometry. Rece","cbCaiq1QrDISMooh","https://ap.wps.com/l/cbCaiq1QrDISMooh","pdf",2345379,4,1,34,"English","en",105,"# Introduction\n## Clinical challenge in aneurysm risk assessment\n## Deep learning approaches and limitations\n## Role of computational fluid dynamics (CFD) and hemodynamic descriptors","[{\"question\":\"What problem does the study address in cerebral aneurysm management?\",\"answer\":\"The study targets the challenge of predicting aneurysm rupture, which requires integrating vascular morphology, local hemodynamics, vessel-wall effects, and patient-specific physiology beyond current imaging-based interpretation.\"},{\"question\":\"How does the proposed pipeline detect aneurysms?\",\"answer\":\"It uses a PointNeXt-based detector to identify aneurysm presence from vascular point clouds, producing predictions for aneurysm-positive cases.\"},{\"question\":\"What inputs are combined for rupture-risk prediction and how is it validated?\",\"answer\":\"Rupture-risk prediction fuses vascular morphology, physics-informed hemodynamic descriptors (e.g., WSS, TAWSS, OSI, RRT) and clinical variables. Performance is reported using pooled AUROC/AUPRC, with the best approach using fixed 70/30 late fusion and statistical comparisons indicating improved results.\"}]",1784207341,86,{"code":4,"msg":31,"data":32},"ok",{"site_id":25,"language":24,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"integrating-physics-informed-neural-networks-and-3d-vascular-geometry-learning-for-cerebral-aneurysm-detection-and-multimodal-rupture-risk-prediction","",{"@graph":36,"@context":85},[37,53,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":20},"https://docshare.wps.com/document/integrating-physics-informed-neural-networks-and-3d-vascular-geometry-learning-for-cerebral-aneurysm-detection-and-multimodal-rupture-risk-prediction/85952/",{"url":52,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":24,"description":14,"dateModified":61,"datePublished":62,"encodingFormat":60,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":41,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-07-25","2026-07-16",true,{"@type":65,"interactionType":66,"userInteractionCount":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What problem does the study address in cerebral aneurysm management?","Question",{"text":75,"@type":76},"The study targets the challenge of predicting aneurysm rupture, which requires integrating vascular morphology, local hemodynamics, vessel-wall effects, and patient-specific physiology beyond current imaging-based interpretation.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the proposed pipeline detect aneurysms?",{"text":80,"@type":76},"It uses a PointNeXt-based detector to identify aneurysm presence from vascular point clouds, producing predictions for aneurysm-positive cases.",{"name":82,"@type":73,"acceptedAnswer":83},"What inputs are combined for rupture-risk prediction and how is it validated?",{"text":84,"@type":76},"Rupture-risk prediction fuses vascular morphology, physics-informed hemodynamic descriptors (e.g., WSS, TAWSS, OSI, RRT) and clinical variables. Performance is reported using pooled AUROC/AUPRC, with the best approach using fixed 70/30 late fusion and statistical comparisons indicating improved results.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]