[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126424-en":3,"doc-seo-126424-105":31,"detail-sidebar-cat-0-en-105":93},{"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":28,"seo_description":14,"update_tm":29,"read_time":30},126424,962085564807,"Aurelia","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A machine learning-based predictive framework for head impact injuries - Research paper","Traumatic brain injury (TBI) remains a major challenge requiring accurate prediction and prevention to reduce its impact across healthcare and engineering. The research develops an artificial neural network (ANN) framework to predict intracranial pressure (ICP), using kinematic linear accelerations (ax, ay, az) and time as inputs. Separate models were trained for five brain regions—frontal, parietal, left temporal, right temporal, and occipital—achieving R=0.98 and RMSE=0.008 MPa. Feedforward and time-series ANNs were trained on 80 samples with 177 timesteps each, demonstrating computationally efficient ICP prediction and supporting clinical translation.","Results in Engineering 28 (2025) 107400  \nContents lists available at ScienceDirect  \nResults in Engineering  \njournal [homepage:](homepage: www.sciencedirect.com/journal/results-in-engineering)[ www.sciencedirect.com/journal/results-in-engineering](homepage: www.sciencedirect.com/journal/results-in-engineering)  \n| Research paper\u003Cbr>A machine learning-based predictive framework for head impact injuries Jing Yi Henga, Rifai Chaib, Saeed Mouloodic, Raj Das d, Kwong Ming Tsea,* \u003Cbr>a Department of Mechanical and Product Design Engineering, Swinburne University of Technology, Melbourne, Australia\u003Cbr>b Department of Telecommunications, Electrical, Robotics and Biomedical Engineering, Swinburne University of Technology, Melbourne, Australia c Department of Mechanical Engineering, University of Melbourne, Melbourne, Australia\u003Cbr>d School of Engineering, RMIT University, Melbourne, Australia |  |  |\n| --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |\n| Keywords:\u003Cbr>Artificial neural networks (ANNs) Biomechanics\u003Cbr>Machine learning Traumatic brain injury (TBI) Finite Element (FE)\u003Cbr>Injury Prediction |  | Traumatic brain injury (TBI) remains a major challenge in both healthcare and engineering, where accurate prediction and prevention strategies are critical to mitigating its impact. Modern technologies, particularly artificial intelligence (AI), have rapidly advanced, offering transformative solutions in this domain. Machine learning (ML), a prominent subset of AI, has proven effective in addressing challenges by discovering intricate relationships within datasets. This research leverages artificial neural networks (ANNs) to predict intracranial pressures, a critical parameter linked to TBI, using kinematic linear accelerations as inputs. The proposed model incorporates three kinematic inputs (ax, ay, az) and time as inputs to the ANN. The developed ANN model successfully predicted intracranial pressure (ICP) values for five distinct brain regions—as frontal, parietal, left temporal, right temporal and occipital—achieving a correlation coefficient (R = 0.98) and root mean square error (RMSE = 0.008 MPa). Both feedforward and time series ANNs were trained on 80 samples with 177 timesteps data each. This study demonstrates the potential of artificial neural networks (ANNs) for accurate and computationally efficient ICP prediction. The findings further underscore the importance of integrating artificial intelligence with biomechanics to advance traumatic brain injury (TBI) research and support its translation into clinical practice. |\n\n1. Introduction  \nTraumatic Brain Injury (TBI) has been recognized as one of the most serious health challenges, affecting >45 million people worldwide [1,2]. Given its severity, it is imperative to prioritize research efforts aimed at comprehensively understanding the profound consequences of severe impacts on the human head. While earlier studies on TBI provided significant insights into underlying injury mechanisms and quantifying injury thresholds under external loading conditions, a precise, universally applicable formula describing the intricate interrelationship of injury metrics remains elusive. This is primarily due to the intricate and heterogeneous nature of human head structures and their responses to diverse loading conditions [3,4].  \nEarlier biomechanical studies using experimental cadaveric work [5–7] have made noteworthy contributions to head injury biomechanics. In particular, these studies provide significant and essential data that aids the development of future biofidelic head surrogates and finite element (FE) head models. However, addressing legal and regulatory challenges is crucial to ensure ethical and proper implementation  \nof cadaveric and animal experiments [8,9].  \nGiven the challenges in measuring internal biomechanical responses of the brain in vivo, FE modelling of the human head provides a costeffective for estimating these responses. Despite the computati","cbCaigKTTiMmeBDV","https://ap.wps.com/l/cbCaigKTTiMmeBDV","pdf",4662307,5,1,11,"English","en",105,"# Abstract\n# Introduction\n## Challenges in TBI prediction\n## Existing biomechanical and FE approaches\n## Need for computational efficiency","[{\"question\":\"What does the proposed framework predict?\",\"answer\":\"It predicts intracranial pressure (ICP), a critical parameter associated with traumatic brain injury (TBI).\"},{\"question\":\"What inputs are used for the artificial neural network?\",\"answer\":\"The model uses three kinematic linear accelerations (ax, ay, az) and time as inputs to the ANN.\"},{\"question\":\"How accurate is the ICP prediction across brain regions?\",\"answer\":\"The study reports strong predictive performance with a correlation coefficient of R=0.98 and RMSE of 0.008 MPa across five brain regions.\"}]","A machine learning-based predictive framework for head impact injuries - Research paper | PDF",1785904982,28,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":88,"head_meta":90,"extra_data":92,"updated_unix":29},"a-machine-learning-based-predictive-framework-for-head-impact-injuries-research-paper","",{"@graph":37,"@context":87},[38,55,70],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,52],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":51},"https://docshare.wps.com/document/research-report/",3,{"item":53,"name":13,"@type":44,"position":54},"https://docshare.wps.com/document/a-machine-learning-based-predictive-framework-for-head-impact-injuries-research-paper/126424/",4,{"url":53,"name":13,"@type":56,"author":57,"headline":13,"publisher":59,"fileFormat":62,"inLanguage":24,"description":14,"dateModified":63,"datePublished":64,"encodingFormat":62,"isAccessibleForFree":65,"interactionStatistic":66},"DigitalDocument",{"name":9,"@type":58},"Person",{"url":42,"name":60,"@type":61},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":67,"interactionType":68,"userInteractionCount":20},"InteractionCounter",{"@type":69},"ViewAction",{"@type":71,"mainEntity":72},"FAQPage",[73,79,83],{"name":74,"@type":75,"acceptedAnswer":76},"What does the proposed framework predict?","Question",{"text":77,"@type":78},"It predicts intracranial pressure (ICP), a critical parameter associated with traumatic brain injury (TBI).","Answer",{"name":80,"@type":75,"acceptedAnswer":81},"What inputs are used for the artificial neural network?",{"text":82,"@type":78},"The model uses three kinematic linear accelerations (ax, ay, az) and time as inputs to the ANN.",{"name":84,"@type":75,"acceptedAnswer":85},"How accurate is the ICP prediction across brain regions?",{"text":86,"@type":78},"The study reports strong predictive performance with a correlation coefficient of R=0.98 and RMSE of 0.008 MPa across five brain regions.","https://schema.org",{"og:url":53,"og:type":89,"og:title":13,"og:site_name":60,"og:description":14},"article",{"robots":91,"canonical":53},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":94},[95,99,103,107,111,116,121,124,129,132,136],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":96,"show_sort_weight":97,"slug":98},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":100,"show_sort_weight":101,"slug":102},"Literature",80,"literature",{"id":54,"doc_module":4,"doc_module_name":47,"category_name":104,"show_sort_weight":105,"slug":106},"Exam",70,"exam",{"id":20,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":47,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":47,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":47,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":47,"category_name":138,"show_sort_weight":20,"slug":139},19,"General","general"]