[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128675-en":3,"doc-seo-128675-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},128675,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","An investigation of the diversity of outcomes along with machine learning in the prediction of ischemia using PET cardiac perfusion imaging","Positron emission tomography (PET) provides precise, non-invasive visualization of cardiovascular function, and ischemia—insufficient blood and oxygen supply—requires accurate detection to reduce serious risk. This thesis investigates how segmentation and analysis of PET cardiac perfusion images on Carimas vary across medical and non-medical backgrounds, and how those differences influence myocardial blood flow (MBF)-based ischemia prediction. Polar maps were generated from 55 stress perfusion images, and MBF comparisons used statistical testing while a pre-trained CNN was validated via k-fold to assess classification performance.","An investigation of the diversity of outcomes along with machine learning in the prediction of ischemia using PET cardiac perfusion imaging  \nNaipunya Guruprasad  \nMaster’s thesis  \nUniversity of Turku, Turku PET Centre Faculty of Medicine, Institute of Biomedicine 06.05.2024  \nMaster´s degree in Biomedical Imaging  \nResearch field: PET imaging and image analysis using machine learning.  \nCredits: 40 ECTS  \nSupervisors:  \n1. Dr. Jarmo Teuho  \nAdjunct Professor, Turku PET Centre, University of Turku  \n2. Prof. Riku Klén  \nAssistant Professor (Imaging instrumentation and detection technologies), Turku PET Centre, University of Turku  \nAbstract  \nUniversity of Turku, Turku PET Centre  \nFaculty of Medicine, Institute of Biomedicine  \nNaipunya Guruprasad: “An investigation of the diversity of outcomes along with machine learning in the prediction of ischemia using PET cardiac perfusion imaging”.  \nMaster’s thesis, 62pp.  \nMaster’s Level  \nMay 2024  \nPositron emission tomography (PET) is an advanced, non-invasive medical imaging technology that allows for precise internal imaging. The recent progress in PET technology, particularly in cardiovascular applications, has significantly improved our knowledge of heart-related conditions. PET scans offer clear visuals of blood circulation and cardiac functions, playing an important role in detecting various conditions. One such condition is ischemia, a state where apart of the body lacks sufficient blood and oxygen supply, posing serious risks. This can be fatal and hence the detection of this is incredibly essential. For accurate predictions of such conditions, healthcare professionals rely on specialised software dedicated to segmentation and visualization of PET cardiac perfusion images, and one such software is Carimas. The integration of predictive machine learning models with Carimas not only enhances but also confirms the accuracy of diagnostic findings, revolutionizing the way we approach cardiac care.  \nThe objective was to have individuals from medical and non-medical backgrounds perform segmentation/analysis on Carimas and to then compare them with one another. Additionally, a pre-trained machine learning model uses the data created on the software to predict whether the patient is ischemic or not. This strategy offers a thorough insight of how segmentation techniques used by people with various backgrounds affect the prediction of ischemia, as well as machine learning models.  \n55 PET cardiac stress perfusion images from ischemic and non-ischemic patients have been used to create polar maps with radiowater [ 15O-H2O] labelling. The Myocardial Blood Flow (MBF) data is compared using statistical tests like the Wilcoxon test, Jaccard Index and Dice Coefficient. The pre-trained The Convolutional Neural Network (CNN) model undergoes K-fold validation to verify its performance.  \nOur study aimed to evaluate the accuracy of ischemia prediction derived from manual segmentation performed by individuals with diverse backgrounds indeed gives different MBF data which in turn affects the classification of patients. The CNN model, on the other hand, shows a high area under the characteristic curve value indicating good performance that is independent of the level of expertise of the individuals who created the polar maps.  \nKeywords: Ischemia, PET Imaging, Carimas, Convolutional neural networks.  \nTable of Contents  \n1. Literature Overview…………………………………………………………………………. 1  \n1.1 Introduction………………………………………………………………………………. 1  \n1.2 Thesis Structure………………………………………………………………………….. 2  \n1.3 Motivation………………………………………………………………………………... 3  \n1.4 Previous studies…………………………………………………………………………. 4  \n2. Aimsand Research Questions………………………………………………………………. 6  \n2.1 Aims ofthe study…………………………………………………………………………6  \n2.2 Research questions……………………………………………………………………….. 6  \n3. Background…………………………………………………………………………………. 7  \n3.1 Positron Emission Tomography………………………………………………………… 7  \n3.1.1 15O-water asa tracer……………………………………","cbCailQuUsYU7OgV","https://ap.wps.com/l/cbCailQuUsYU7OgV","pdf",2777324,1,68,"English","en",105,"# Literature Overview\n## Introduction\n## Thesis Structure\n## Motivation\n## Previous studies\n# Aims and Research Questions\n## Aims of the study\n## Research questions\n# Background\n## Positron Emission Tomography\n## Myocardial Ischemia\n## Carimas\n## PET imaging for the diagnosis of myocardial ischemia\n## AI for Cardiovascular diseases\n## Image classification and ML models\n# Materials and Methods\n## Patient selection\n## Image acquisition\n## Data acquisition protocols\n## Workflow\n## Statistical tests\n## Coding\n# Results\n## Medical vs Non-Medical Expert\n## Classification Results\n## K-fold results\n# Discussion\n## Future studies\n# Conclusion\n# Acknowledgement\n# References","[{\"question\":\"What is the main objective of the thesis?\",\"answer\":\"To assess whether manual segmentation performed by individuals with diverse backgrounds leads to different MBF data and, consequently, impacts ischemia prediction. It also evaluates whether a CNN classification model performs robustly regardless of the experts’ segmentation background.\"},{\"question\":\"How were PET cardiac perfusion images used in the study?\",\"answer\":\"Fifty-five PET cardiac stress perfusion images from ischemic and non-ischemic patients were processed to create polar maps using 15O-water labeling. MBF data were then compared using statistical tests such as Wilcoxon, Jaccard Index, and Dice Coefficient.\"},{\"question\":\"What method validated the machine learning model’s performance?\",\"answer\":\"A pre-trained convolutional neural network (CNN) underwent k-fold validation to verify classification performance for ischemia prediction.\"}]","An investigation of the diversity of outcomes along with machine learning in the prediction of ischemia using PET cardiac perfusion imaging | PDF",1786002494,171,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"an-investigation-of-the-diversity-of-outcomes-along-with-machine-learning-in-the-prediction-of-ischemia-using-pet-cardiac-perfusion-imaging","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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":53},"https://docshare.wps.com/document/an-investigation-of-the-diversity-of-outcomes-along-with-machine-learning-in-the-prediction-of-ischemia-using-pet-cardiac-perfusion-imaging/128675/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-22","2026-08-06",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the main objective of the thesis?","Question",{"text":76,"@type":77},"To assess whether manual segmentation performed by individuals with diverse backgrounds leads to different MBF data and, consequently, impacts ischemia prediction. It also evaluates whether a CNN classification model performs robustly regardless of the experts’ segmentation background.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were PET cardiac perfusion images used in the study?",{"text":81,"@type":77},"Fifty-five PET cardiac stress perfusion images from ischemic and non-ischemic patients were processed to create polar maps using 15O-water labeling. MBF data were then compared using statistical tests such as Wilcoxon, Jaccard Index, and Dice Coefficient.",{"name":83,"@type":74,"acceptedAnswer":84},"What method validated the machine learning model’s performance?",{"text":85,"@type":77},"A pre-trained convolutional neural network (CNN) underwent k-fold validation to verify classification performance for ischemia prediction.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,124,129,132,136],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":46,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":46,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":122,"slug":123},30,"research-report",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":126,"show_sort_weight":127,"slug":128},9,"Religion & Spirituality",20,"religion-spirituality",{"id":127,"doc_module":4,"doc_module_name":46,"category_name":130,"show_sort_weight":127,"slug":131},"World Cup","world-cup",{"id":133,"doc_module":4,"doc_module_name":46,"category_name":134,"show_sort_weight":133,"slug":135},10,"Lifestyle","lifestyle",{"id":137,"doc_module":4,"doc_module_name":46,"category_name":138,"show_sort_weight":107,"slug":139},19,"General","general"]