Cardiovascular disease under the lens of automated 3D analysis of the heart
Improving healthcare systems mandates a shift towards personalised and preventive medicine, especially in the management of cardiovascular diseases, the world leading cause of death. The anatomy and function of the heart can be accurately and non-invasively assessed using cardiac magnetic resonance....
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| Format: | Thesis |
| Language: | English |
| Published: |
2021
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| _version_ | 1797112608437829632 |
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| author | Corral Acero, J |
| author2 | Grau Colomer, V |
| author_facet | Grau Colomer, V Corral Acero, J |
| author_sort | Corral Acero, J |
| collection | OXFORD |
| description | Improving healthcare systems mandates a shift towards personalised and preventive medicine, especially in the management of cardiovascular diseases, the world leading cause of death. The anatomy and function of the heart can be accurately and non-invasively assessed using cardiac magnetic resonance. Nevertheless, it is not yet fully understood how their interplay modulates disease outcomes. In this regard, this Thesis (1) reviews the crucial role of computational modelling towards precision cardiology, by building the ‘digital twin’ of a patient; (2) develops a fully automated pipeline to construct 3D computational models of human left ventricle from 2D cardiac magnetic images, using state-of-art deep learning approaches; and (3) proposes an enhanced structural and functional analysis to disentangle how adverse outcomes are modulated in cardiovascular disease. This is showcased in acute myocardial infarction, the deadliest cardiovascular disease not only on first manifestation, but on reoccurrence and secondary adverse outputs. In this scenario, the approach introduced in this Thesis is shown to provide additional prognostic information towards a better post-infarction management. Moreover, it identifies 3D shape and contraction patterns that are related to adverse outcomes, building a more profound understanding of remodelling after infarction. Furthermore, the automation of the analysis removes the burden of manual annotations and intra- and inter-observer variability, as confounders of prognosis. All this argues for the clinical impact of this Thesis. Ultimately, the Thesis evidences the potential of 3D computational models for cardiovascular disease management, towards personalised and preventive care. |
| first_indexed | 2024-03-07T08:27:57Z |
| format | Thesis |
| id | oxford-uuid:a29845d4-a3e2-40d0-a855-fa4738b7fc88 |
| institution | University of Oxford |
| language | English |
| last_indexed | 2024-03-07T08:27:57Z |
| publishDate | 2021 |
| record_format | dspace |
| spelling | oxford-uuid:a29845d4-a3e2-40d0-a855-fa4738b7fc882024-02-22T09:49:46ZCardiovascular disease under the lens of automated 3D analysis of the heartThesishttp://purl.org/coar/resource_type/c_db06uuid:a29845d4-a3e2-40d0-a855-fa4738b7fc88Personalised medicineBioengineeringCardiologyDigital twinsMedical ImagingEnglishHyrax Deposit2021Corral Acero, JGrau Colomer, VBueno-Orovio, ALamata, PImproving healthcare systems mandates a shift towards personalised and preventive medicine, especially in the management of cardiovascular diseases, the world leading cause of death. The anatomy and function of the heart can be accurately and non-invasively assessed using cardiac magnetic resonance. Nevertheless, it is not yet fully understood how their interplay modulates disease outcomes. In this regard, this Thesis (1) reviews the crucial role of computational modelling towards precision cardiology, by building the ‘digital twin’ of a patient; (2) develops a fully automated pipeline to construct 3D computational models of human left ventricle from 2D cardiac magnetic images, using state-of-art deep learning approaches; and (3) proposes an enhanced structural and functional analysis to disentangle how adverse outcomes are modulated in cardiovascular disease. This is showcased in acute myocardial infarction, the deadliest cardiovascular disease not only on first manifestation, but on reoccurrence and secondary adverse outputs. In this scenario, the approach introduced in this Thesis is shown to provide additional prognostic information towards a better post-infarction management. Moreover, it identifies 3D shape and contraction patterns that are related to adverse outcomes, building a more profound understanding of remodelling after infarction. Furthermore, the automation of the analysis removes the burden of manual annotations and intra- and inter-observer variability, as confounders of prognosis. All this argues for the clinical impact of this Thesis. Ultimately, the Thesis evidences the potential of 3D computational models for cardiovascular disease management, towards personalised and preventive care. |
| spellingShingle | Personalised medicine Bioengineering Cardiology Digital twins Medical Imaging Corral Acero, J Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title | Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title_full | Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title_fullStr | Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title_full_unstemmed | Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title_short | Cardiovascular disease under the lens of automated 3D analysis of the heart |
| title_sort | cardiovascular disease under the lens of automated 3d analysis of the heart |
| topic | Personalised medicine Bioengineering Cardiology Digital twins Medical Imaging |
| work_keys_str_mv | AT corralaceroj cardiovasculardiseaseunderthelensofautomated3danalysisoftheheart |