[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"detail-sidebar-cat-0-en-105":3,"doc-seo-124569-105":59,"doc-detail-124569-en":131},{"code":4,"msg":5,"data":6},0,"success",[7,13,18,23,28,33,38,43,48,51,55],{"id":8,"doc_module":4,"doc_module_name":9,"category_name":10,"show_sort_weight":11,"slug":12},1,"Document","Story & Novel",90,"story-novel",{"id":14,"doc_module":4,"doc_module_name":9,"category_name":15,"show_sort_weight":16,"slug":17},2,"Literature",80,"literature",{"id":19,"doc_module":4,"doc_module_name":9,"category_name":20,"show_sort_weight":21,"slug":22},4,"Exam",70,"exam",{"id":24,"doc_module":4,"doc_module_name":9,"category_name":25,"show_sort_weight":26,"slug":27},5,"Comic",60,"comic",{"id":29,"doc_module":4,"doc_module_name":9,"category_name":30,"show_sort_weight":31,"slug":32},6,"Technology",50,"technology",{"id":34,"doc_module":4,"doc_module_name":9,"category_name":35,"show_sort_weight":36,"slug":37},7,"Healthcare",40,"healthcare",{"id":39,"doc_module":4,"doc_module_name":9,"category_name":40,"show_sort_weight":41,"slug":42},8,"Research & Report",30,"research-report",{"id":44,"doc_module":4,"doc_module_name":9,"category_name":45,"show_sort_weight":46,"slug":47},9,"Religion & Spirituality",20,"religion-spirituality",{"id":46,"doc_module":4,"doc_module_name":9,"category_name":49,"show_sort_weight":46,"slug":50},"World Cup","world-cup",{"id":52,"doc_module":4,"doc_module_name":9,"category_name":53,"show_sort_weight":52,"slug":54},10,"Lifestyle","lifestyle",{"id":56,"doc_module":4,"doc_module_name":9,"category_name":57,"show_sort_weight":24,"slug":58},19,"General","general",{"code":4,"msg":60,"data":61},"ok",{"site_id":62,"language":63,"slug":64,"title":65,"keywords":66,"description":67,"schema_data":68,"social_meta":124,"head_meta":126,"extra_data":128,"updated_unix":130},105,"en","can-machine-learning-predict-particle-deposition-at-specific-intranasal-regions-based-on-computational-fluid-dynamics-inputs-and-nasal-geometry-measurements-study","Can Machine Learning Predict Particle Deposition at Specific Intranasal Regions Based on Computational Fluid Dynamics Inputs and Nasal Geometry Measurements - study","","Machine learning can be used to estimate where inhaled particles deposit in the upper airway, using computational fluid dynamics (CFD) outputs and patient nasal geometry measurements. A protocol was proposed to map nasal-spray formulation parameters to device settings for effective drug delivery, addressing the limited availability of patient data across diverse demographics. Eleven anatomical parameters were measured from 10 CT-based nasal geometries, while 160 CFD simulations modeled drug delivery under varied breathing scenarios and pressure gradients. Eighteen machine-learning models were trained to predict deposition across upper-airway regions.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":40,"@type":76,"position":81},"https://docshare.wps.com/document/research-report/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/can-machine-learning-predict-particle-deposition-at-specific-intranasal-regions-based-on-computational-fluid-dynamics-inputs-and-nasal-geometry-measurements-study/124569/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/can-machine-learning-predict-particle-deposition-at-specific-intranasal-regions-based-on-computational-fluid-dynamics-inputs-and-nasal-geometry-measurements-study/124569.png","ImageObject",300,407,{"name":92,"@type":93},"Jiven","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-17","2026-08-05",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",12,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What problem does the study address for intranasal drug delivery?","Question",{"text":113,"@type":114},"It targets improved, patient-aligned deposition in upper airway regions by linking nasal spray design parameters and device-relevant settings to predicted drug placement.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"How were nasal geometry measurements obtained and used?",{"text":118,"@type":114},"Eleven anatomical parameters were measured from 10 CT-based nasal geometries representing the population to parameterize the delivery scenario.",{"name":120,"@type":111,"acceptedAnswer":121},"What data and modeling approach produced deposition predictions?",{"text":122,"@type":114},"The study ran 160 CFD simulations across breathing situations and pressure gradients, then trained 18 machine-learning models to estimate targeted deposition in different upper-airway regions.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},124569,1785893040,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":39,"category_name":40,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":8,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":130,"read_time":81},962085662650,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Can machine learning predict particle deposition at specific intranasal regions based on computational fluid dynamics inputs/outputs and nasal geometry measurements?  \nMohammad Mehedi Hasan Akash 1, Zachary Silfen2, Diane Joseph-McCarthy2, Arijit Chakravarty3, Saikat Basu 1  \n1 Department of Mechanical Engineering, South Dakota State University, Brookings, SD  \n2 Department of Biomedical Engineering, Boston University, Boston, MA  \n3 Fractal Therapeutics, Cambridge, MA  \nAlong with machine learning modeling, numerical simulations of respiratory airflow and particle transport can be used to improve targeted deposition at the upper respiratory infection site of numerous airborne diseases. Given the need for more patient data from varied demographics, we propose a machine learning-enabled protocol for determining optimal formulation design parameters that may match nasal spray device settings for successful drug delivery. We measured 11 anatomical parameters (including nasopharyngeal volume, nostril heights, and mid-nasal cavity volume) for 10 CT-based nasal geometries representative of the population for this aim. We also ran 160 computational fluid dynamics simulations of drug delivery on the same geometries for various breathing situations, using varied pressure gradientsto drive inhaled air transport to evaluate drug deposition at the various upper airway areas for nasal inhalers. Using this test data, we constructed 18 machine-learning models to estimate the targeted deposition at the different regions of the upper airway. This study contributes to developing a customized, efficient intranasal delivery system for prophylactics, treatments, and immunizations; the findings will apply to a broad spectrum of respiratory disorders.","cbCaiauxRrGJ1VsR","https://ap.wps.com/l/cbCaiauxRrGJ1VsR","pdf",60462,"English","# Machine-learning prediction for intranasal deposition\n## Study rationale and goal\n## Anatomical measurements from CT geometries\n## CFD simulations and deposition evaluation\n## Model training and regional prediction","[{\"question\":\"What problem does the study address for intranasal drug delivery?\",\"answer\":\"It targets improved, patient-aligned deposition in upper airway regions by linking nasal spray design parameters and device-relevant settings to predicted drug placement.\"},{\"question\":\"How were nasal geometry measurements obtained and used?\",\"answer\":\"Eleven anatomical parameters were measured from 10 CT-based nasal geometries representing the population to parameterize the delivery scenario.\"},{\"question\":\"What data and modeling approach produced deposition predictions?\",\"answer\":\"The study ran 160 CFD simulations across breathing situations and pressure gradients, then trained 18 machine-learning models to estimate targeted deposition in different upper-airway regions.\"}]","Can Machine Learning Predict Particle Deposition at Specific Intranasal Regions Based on Computational Fluid Dynamics Inputs and Nasal Geometry Measurements - study | PDF"]