Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging - Thesis

Aug 3, 2026

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Explores how machine learning–based artificial intelligence can be applied to analyze cardiovascular imaging to strengthen clinical decision-making. Summarizes core ML-AI concepts and reviews advances across data processing, image analysis, result interpretation, and emerging clinical implementations. Covers hybrid cardiac imaging workflows such as view identification, structure segmentation, disease identification, functional parameter estimation, and prognostic evaluation, emphasizing opportunities for automation and individualized clinical decision support aligned with routine practice.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages33

Reading Time1 h 23 min

AccessFree to download

Abstract

Introduction

Hybrid imaging concept and benefits

Medical image analysis with ML-based AI

University of Groningen Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging Benjamins, Jan-Walter DOI: 10.33612/diss.844209572 IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below. Document Version Publisher's PDF, also known as Version of record Publication date: 2024 Link to publication in University of Groningen/UMCG research database Citation for published version (APA): Benjamins, J.-W. (2024) . Exploring the applicability of machine learning based artificial intelligence in the analysis of cardiovascular imaging. [Thesis fully internal (DIV), University of Groningen] . University of Groningen. [https://doi.org/10.33612/diss.844209572](https://doi.org/10.33612/diss.844209572) Copyright Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons) . The publication may also be distributed here under the terms of Article 25fa of the Dutch Copyright Act, indicated by the “Taverne” license. More information can be found on the University of Groningen website: [https://www.rug.nl/library/open-access/self-archiving-pure/taverne](https://www.rug.nl/library/open-access/self-archiving-pure/taverne)amendment. Take-down policy If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from the University of Groningen/U MCG research database (Pure): [http://www.rug. nl/research/portal. For technical reasons the](http://www.rug. nl/research/portal. For technical reasons the)[ ](http://www.rug. nl/research/portal. For technical reasons the)[number of authors shown on this cover page is limited to 10 maximum.](number of authors shown on this cover page is limited to 10 maximum.) Download date: 29-12-2025 3 HYBRID CARDIAC IMAGING: THE ROLE OF MACHINE LEARNING AND ARTIFICIAL INTELLIGENCE J.W. Benjamins, M.W. Yeung, A.E. Reyes-Quintero, B. Ruijsink, P. van der Harst, L.E. Juarez-Orozco Hybrid Cardiac Imaging for Clinical Decision-Making, 203–222, 19-08-2022 Abstract Machine learning currently represents the corner stone of modern artificial intelligence. The algorithms involved have rapidly permeated into medical sciences and have demonstrated the capacity to revolutionize data analysis through optimized variable exploration and integration as well as improved image processing and recognition. As such, cardiovascular hybrid imaging constitutes an open pathway for implementation in the form of view identification, structure segmentation, disease identification, functional parameter estimation and prognostic evaluation in is traditional forms in SPECT/CT, PET/ CT and PET/MR. Further, an elastic view on the concept of hybridization in cardiovascular imaging offers the possibility to concatenate applications based on the combination of machine learning models, data types and imaging modalities. Current aims for these implementations include process automation and generation of clinical decision support systems tailored to the needs of daily clinical practice in the evaluation of cardiovascular disease at the individual level. The present chapter summarizes core concepts in modern machine learning-based AI, provides an overview of the recent advances in data processing, image analysis, result interpretation and emerging clinical implementations, and suggests the potential and future perspectives of machine learning analytics within the context of hybrid cardiovascular imaging. Introduction The concept of hybrid imaging has long lingered in the minds of cardiovascular and imaging physicians. The benefit of combining ima

FAQs

What is the main goal of using ML-based AI in cardiovascular imaging?

To improve analysis workflows and support clinical decision-making by enabling tasks like segmentation, identification, functional estimation, and prognostic evaluation.

Which hybrid imaging settings does the text highlight?

Combinations such as SPECT/PET for myocardial perfusion with CT for coronary anatomy, and PET with CMR for tissue characterization, along with broader anatomical-functional mapping contexts.

What challenge motivates ML-based AI adoption in this area?

The major challenge is the time and labor required to process and interpret the rapidly growing volume of medical imaging data.

Aug 3, 2026

Upload by Eliana