Welcome to the UPF Digital Repository

Automatic aetiology identification of left ventricular hypertrophy from magnetic resonance cine sequences

Show simple item record

dc.contributor.author Clua i Sánchez, Estel
dc.date.accessioned 2023-09-22T16:40:13Z
dc.date.available 2023-09-22T16:40:13Z
dc.date.issued 2023-09-22
dc.identifier.uri http://hdl.handle.net/10230/57943
dc.description Tutors: César Acebes, Marian Iglesias, Pr. Oscar Camara. Treball de fi de grau en Biomèdica
dc.description.abstract Left ventricular hypertrophy (LVH) consists of the thickening of the myocardium, significantly increasing the risk of cardiovascular events. LVH can be attributed to various etiologies, each with distinct prognoses and treatments. Cardiac magnetic resonance (CMR) is the principal non-invasive tool for diagnosing LVH, being cine sequences the gold standard for left ventricle characterization. However, additional time-consuming sequences are required for pathology diagnosis. Artificial intelligence (AI) has demonstrated potential in CMR analysis, creating a place for the development of AI-based automatic tools to assess the underlying aetiology of LVH using cine CMR images. This thesis aims to develop an AI-based algorithm to classify the main etiologies of LVH using cine CMR images and clinical data using a clinical database (N=304). Given the imbalanced distribution of data, two versions of the model were implemented: multi-class and binary classification. Simultaneously, the integration of automated segmentation within the classification algorithm was evaluated, utilizing a pre-trained model. Binary and multi-class classifications yielded accuracies of 69% and 62%, respectively. Segmentation results (Dice coefficient: 76.58%) underscore the necessity of a well-curated dataset that includes ground truth segmentations. Saliency maps revealed that misclassifications were primarily attributed to the algorithm’s inability to focus on myocardium. The proposed workflow represents a step forward in AI integration within clinical workflows. It is an end-to-end model ready for deployment, enabling an automated classification of LVH aetiologies in the point-of-care. Overall, this technology has been introduced to Hospital de Sant Pau being a significant advancement in enhancing diagnostic efficiency reducing costs and time.
dc.format.mimetype application/pdf
dc.language eng
dc.language.iso eng
dc.rights Llicència CC Reconeixement-NoComercial-SenseObraDerivada 4.0 Internacional (CC BY-NC-ND 4.0)
dc.rights.uri https://creativecommons.org/licenses/by-nc-nd/4.0/deed.ca
dc.title Automatic aetiology identification of left ventricular hypertrophy from magnetic resonance cine sequences
dc.type info:eu-repo/semantics/bachelorThesis
dc.subject.keyword Left ventricular hypertrophy
dc.subject.keyword Cardiac magnetic resonance
dc.subject.keyword Cine sequences
dc.subject.keyword Convolutional neural network
dc.subject.keyword Aetiology classification
dc.rights.accessRights info:eu-repo/semantics/openAccess


This item appears in the following Collection(s)

Show simple item record

Search DSpace

Advanced Search


My Account


In collaboration with Compliant to Partaking